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In this paper, an infinite-horizon optimal tracking control scheme is proposed for a class of nonlinear discrete-time switched systems. First, via system transformation, the optimal tracking problem is converted into designing an optimal regulator for the tracking error dynamics. And then, with convergence analysis in terms of value function and control policy, the iterative adaptive dynamic programming (ADP) algorithm is introduced to obtain the infinite-horizon optimal tracking controller which makes the value function close to its optimal value function. Next, two neural networks are used as parametric structures to implement the ADP algorithm, which aim at approximating the value function and the control policy, respectively. Finally, a simulation example is included to complement the theoretical discussions. [ABSTRACT FROM AUTHOR]

This paper studies the monotonic type-2 fuzzy neural network (T2FNN), which can be adopted in many identification and prediction problems where the monotonicity property between the inputs and outputs is required. Sufficient conditions on the parameters of the T2FNN are first presented to ensure the monotonicity between the inputs and outputs. Then, data-driven design model for the monotonic T2FNN is built. Also, under the monotonicity constraints, a hybrid algorithm is provided to optimize the parameters of the monotonic T2FNN. This hybrid algorithm utilizes the constrained least squares method and the penalty function-based gradient descent algorithm to realize reasonable parameter initialization and optimization. At last, an application to the thermal comfort index prediction is given to verify the effectiveness of the monotonic T2FNN. Comparisons with other methods are also made. [ABSTRACT FROM AUTHOR]

This paper describes a very detailed and comprehensive description of a model predictive controller (MPC) applied to the buck converter working in Continuous Conduction Mode (CCM) to optimize the performance of the converter. The converter is designed for a fixed switching frequency of 1 MHz. The proposed model predictive control technique achieves improved set point tracking with minimal overshoot, offers consistent closed loop performance and has short settling time. Although MPC rejects unmeasured disturbances effectively, but it shows poor load and line regulation. Solution has been suggested in order to robustify the load and line regulation. Further neural network based MPC has also been designed. The impact of the MPC parameters such as prediction and control horizons, performance weights, and constraints on the performance is minutely observed. In order to investigate the effectiveness of the proposed algorithms, MPC is compared with the classical well-known PWM-PID controller. MATLAB/Simulink simulation results validate the control design procedure. [ABSTRACT FROM AUTHOR]

Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect. [ABSTRACT FROM AUTHOR]

Neural network based on back-propagation (BP) algorithm is a widely used prediction model. However, the nodes number of the first hidden layer, the learning rate and momentum factor are usually determined manually, which affects the forecast accuracy of network. Therefore, in this paper, to improve the forecast accuracy, firstly, the nodes number of the first hidden layer is selected adaptively based on minimizing mean square error (MSE). Secondly, improved genetic algorithm (GA) is proposed to train the learning rate and momentum factor dynamically, which includes multi-point crossover and single point mutation. Thirdly, we construct a new neural network model based on the adaptively selected nodes number of the first hidden layer, the dynamically selected learning rate and momentum factor, which is called HN-GA-BP neural network model. Finally, the proposed neural network model is used to forecast the carbon dioxide contents in China for fifty years. Experimental results demonstrate the effectiveness of the proposed HN-GA-BP neural network model. [ABSTRACT FROM AUTHOR]

This paper presents a review of target detection and classification in forward scattering radar (FSR) which is a special state of bistatic radars, designed to detect and track moving targets in the narrow region along the transmitter-receiver base line. FSR has advantages and incredible features over other types of radar configurations. All previous studies proved that FSR can be used as an alternative system for ground target detection and classification. The radar and FSR fundamentals were addressed and classification algorithms and techniques were debated. On the other hand, the current and future applications and the limitations of FSR were discussed. [ABSTRACT FROM AUTHOR]

The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a neural network-based method to generate multiple images with different exposures from a single input low dynamic range (LDR) image for improved high dynamic range (HDR) imaging. The proposed algorithm consists of three steps: 1) 2-D histogram estimation; 2) neural network-based LDR images estimation; and 3) generation of an optimal set of differently exposed images. The proposed method first generates image features by estimating a patched-based 2-D histogram. The extracted features are used in an input layer of the neural network, which plays a role to select an optimal set of LDR images. A set of LDR images is generated using a curvature-based contrast enhancement method. Experimental results show that the proposed method can generate an optimal set of LDR images using neural network and provide improved HDR images. In addition, the proposed method can be implemented as a preprocessing step in most existing HDR frameworks. The proposed HDR approach is considered as a single-input method that gives almost the same performance to multiple image-based HDR method. [ABSTRACT FROM PUBLISHER]

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of a three-phase voltage source pulse width modulation rectifier in the presence of parametric uncertainties and external time-varying disturbances. An adaptive controller is designed by combining a modified dynamic surface control method and a predictor-based iterative neural network control algorithm. Especially, neural networks with iterative update laws based on prediction errors are employed to identify the lumped uncertainties. Besides, a finite-time-convergent differentiator, instead of a first-order filter, is used to obtain the time derivative of the virtual control law. Using a Lyapunov-Krasovskii functional, it is proved that all signals in the closed-loop system are ultimately uniformly bounded. Both simulation and experimental studies are provided to show the effectiveness of the proposed approach. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The following paper presents the study and simulation of energy in the implementation of electric motors, supported with photovoltaic panels, and using a vehicle prototype, based on consumption data and external variables that influence the wear and the increased use offossil fuels in conventional vehicles. A road section is studied in the city of Bogota to acquire relevant information for use within the simulation such as speed, distance in displacements, solar radiation, etc. The algorithm intends to predict the energy impact presented in a road section, if it is implemented in conventional vehicles in order to reduce pollution, the consumption of fossil fuels and improve the automobile efficiency. [ABSTRACT FROM AUTHOR]

In this article, I cumulate previous research findings indicating that, by leveraging natural language processing, sentiment analytics, business intelligence tools, data-driven decision making, and real-time Internet of Things data, online and virtual marketplaces configure customer loyalty and demand, shopping habits, and changing customer order patterns, while customizing user experiences. I contribute to the literature on predictive algorithms, data visualization tools, and artificial neural networks in the retail metaverse by showing that natural language processing and data sharing technologies, together with data visualization capabilities and sentiment analysis, can enhance business performance in livestreaming e-commerce, configuring consumer purchasing habits and improving customer expectations, confidence, loyalty, and engagement. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "retail metaverse" + "predictive algorithms," "data visualization tools," and "artificial neural networks." As I inspected research published in 2022, only 84 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 17, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimization problems which directed by the fitness values. In this paper, we propose one efficient fractional global learning machine (Fragmachine) which includes two stages (descending and ascending) to determine the optimal search path. The neural network model is used to approach the given fitness value. Specifically, for the descending stage, the integer gradient of the network output with respect the current location is employed to find the next descending point, while for the ascending stage, the fractional gradient is implemented to climb and escape from the local optimal point. We further propose one adaptive learning rate during training which relies on both the current gradient (integer or fractional) information and the fitness value. Finally, a series of numerical experiments verify the effectiveness of the proposed algorithm, Fragmachine. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms (DWT) and back-propagation neural networks for detection and classification of internal faults in a two-winding three-phase transformer . Fault conditions of the transformer are simulated using Electromagnetic Transients Program (EMTP) in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented on MATLAB. In addition, the initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems. [ABSTRACT FROM AUTHOR]

In this paper, a neural network is trained and validated using a low end and inexpensive microcontroller. The well-known backpropagation algorithm is implemented to train a neural network model. Both the training and the validation parts are shown through an alphanumeric liquid crystal display. A chemical process was chosen as a realistic nonlinear system to demonstrate the feasibility, and the performance of the results found using the microcontroller. A comparison was made between the microcontroller and the computer results. [ABSTRACT FROM AUTHOR]

The larger display devices, the more noticeable artifacts such as false contours, block artifacts, and other types of noises. This paper proposes a false contour reduction algorithm using neural networks (NNs) and adaptive bidirectional smoothing. The proposed algorithm consists of two parts: false contour detection and reduction parts. In the false contour detection part, false contour candidate pixels are detected using the directional contrast features. The false contour reduction part is composed of two steps: NN processing and bi-directional filtering. In the first step, false contours are reduced by pixelwise processing using NNs. In the second step, bi-directional smoothing is applied to a neighboring region of the false contour. Computer simulations with several test images show the effectiveness of the proposed false contour reduction algorithm in terms of the visual quality of result images, edge maps detected by Sobel masks, the peak signal-to-noise ratio, the structural similarity, and the computation time. [ABSTRACT FROM AUTHOR]

This paper presents a generalized GM(1,1) model based on the traditional GM (1,1) model firstly. Then it combines the generalized GM(1,1) model and the theory of neural network to establish a new forecasting model, i.e. GGNNM(1,1) model. Thirdly, it gives an algorithm for solving this new model. Finally, this new model is applied in forecasting the amount of mine gas emission. The forecasting results show that the GGNNM(1,1) model has the advantages of high forecasting precision and good applicability. [ABSTRACT FROM AUTHOR]

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm (λ -ICP) that uses a multiparameter functional (λ -functional). The proposed λ -ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the λ -functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed λ -ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP). [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm with neuron-by-neuron computation methods for the gradient vector and the Jacobian matrix. The algorithm can handle networks with arbitrarily connected neurons. The training speed is comparable with the Levenberg-Marquardt algorithm, which is currently considered by many as the fastest algorithm for neural network training. More importantly, it is shown that the computation of the Jacobian, which is required for second-order algorithms, has a similar computation complexity as the computation of the gradient for first-order learning methods. This new algorithm is implemented in the newly developed software, Neural Network Trainer, which has unique capabilities of handling arbitrarily connected networks. These networks with connections across layers can be more efficient than commonly used multilayer perceptron networks. [ABSTRACT FROM AUTHOR]

This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures. Instead of the conventional flat vector representation for a face, a neural network approach-based technique is proposed to transform the Localised Gabor Feature (LGF) vectors extracted from human facial components into Human Face Tree Structure (HFTS) to represent a human face. A structural training algorithm is assigned to train and recognize the face identity in this HFTS representation with the corresponding LGF vectors. By benchmarking using the tested public face databases presented in this paper, our approach is able to achieve accuracy up to 90% under different scenarios of lighting conditions and posture orientations. [ABSTRACT FROM AUTHOR]

Fault detection is an essential part of the operation of any chemical plant. Early detection of faults is important in chemical industry since a lot of damage and loss can result before a fault present in the system is detected. Even though fault detection algorithms are designed and implemented for quickly detecting incidents, most these algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. Based on the optimization property of cumulative sum (CUSUM), a real-time system for detecting changes in dynamic systems is designed in this paper. This work is motivated by combining two fault detection (FD) strategies; a simplified procedure of the incident detection problem is formulated by using both the artificial neural networks (ANN) and the CUSUM statistical test (Page–Hinkley test). The design of a model-based residual generator is intended to reveal any drift from the normal behavior of the process. In order to obtain a reliable model for the normal process dynamics, the neural black-box modeling by means of a nonlinear auto-regressive with eXogenous input (NARX) model has been chosen in this study. This paper also shows the choice and the performance of the neural network in the training and test phases. After describing the system architecture and the proposed methodology of the fault detection, we present a realistic application in order to show the technique’s potential. The purpose is to develop and test the fault detection method on a real incident data, to detect the change presence, and pinpoint the moment it occurred. The experimental results demonstrate the robustness of the FD method. [ABSTRACT FROM AUTHOR]

Continuum or hyper-redundant robot manipulators can exhibit behavior similar to biological trunks, tentacles, or snakes. Unlike traditional rigid-link robot manipulators, continuum robot manipulators do not have rigid joints, hence these manipulators are extremely dexterous, compliant, and are capable of dynamic adaptive manipulation in unstructured environments. However, the development of high-performance control algorithms for these manipulators is quite a challenge, due to their unique design and the high degree of uncertainty in their dynamic models. In this paper, a controller for continuum robots, which utilizes a neural network feedforward component to compensate for dynamic uncertainties is presented. Experimental results using the OCTARM, which is a soft extensible continuum manipulator, are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved performance. [ABSTRACT FROM AUTHOR]

In the paper, a method of precision motion control for permanent magnet linear motors is proposed. Unlike rotational motors, permanent magnetic linear motors are more sensitive to various force disturbances because of the reduction of gears. So, as a feedback compensator, a disturbance observer is used to compensate the force disturbances based on the disturbance model. But the force disturbances of permanent magnetic linear motors cannot be fully compensated owing to the error of dynamic model, inaccurately detected velocity and acceleration, especially when a permanent magnetic linear motor runs in low speed. Further analysis shows that the force ripple is the main force disturbance when the velocity of a PMLM is close to zero, and the disturbance model denotes that the force ripple is position dependent. In order to further suppress the force disturbances of permanent magnetic linear motors a feedforward neural network using the BP algorithm is proposed to approximate and compensate the force ripple. The experimental results show that the force ripple is efficiently alleviated and the high positioning precision can be achieved by using the proposed precision motion control method. [ABSTRACT FROM AUTHOR]

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real- world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi et al. to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced. [ABSTRACT FROM AUTHOR]

In this paper, the recurrent neural networks (RNNs) are applied to compensate for hidden Marker model (HMM) recognition algorithm, which is commonly used as a main recognizer. Among these RNNs, the multilayer recurrent neural prediction model (MRNPM), which allows operating in real-time, is used to implement learning and recognition, and HMM and MRNPM are used to design a hybrid type main recognizer. After testing the designed speech recognition algorithm with Korean number pronunciations (13 words), which are hardly distinct, for its speech-independent recognition ratio, about a 5% improvement is obtained comparing results with existing HMM recognizers. Based on this result, only optimal (recognition) codes were extracted in the actual DSP (TMS320C6711) environment and the embedded speech recognition system was implemented. Similarly, the implementation result of the embedded system showed an improved recognition system implementation than existing solid HMM recognition systems. [ABSTRACT FROM AUTHOR]

In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models. [ABSTRACT FROM AUTHOR]

The performance of MPEG video transmission over error-prone channels is limited by the channel noise. An efficient error concealment (EC) scheme is essential for diminishing the impact of transmission errors in a compressed video. A number of EC techniques have been developed to combat the transmission errors. However, the previous techniques are always inefficient when the motions of video object are fast or complex. This paper proposes a novel adaptive EC algorithm to conceal the error for block and motion-compensation based video coding systems. The proposed EC method employs an unsupervised artificial neural network (ANN) model, i.e. self-organizing map (SOM), as a predictor to estimate the motion vectors of the damaged macroblocks (MBs). Then the estimated motion vectors were utilized to reconstruct the damaged MB by exploiting the spatial information from reference frames based on the boundary matching criterion. Because of the SOM has a great capacity for visualizing and interpreting high-dimensional data sets, the estimation model proposed herein can exploit the nonlinearity property of the SOM to estimate lost motion vectors more accurately. Computer simulations show that the visual quality and the PSNR evaluation of reconstructed frames are significantly improved by using the proposed EC algorithm. Thus, the proposed algorithm is expected to be practical for motion vector compressed video in error-prone networks. [ABSTRACT FROM AUTHOR]

Changing the resolution of digital images and video is needed image processing systems. In this paper, we present nonlinear interpolation schemes for still image resolution enhancement. The proposed neural network interpolation method is based on wavelet reconstruction. With the wavelet decomposition, the image signals can be divided into several time--frequency portions. In this work, the wavelet decomposition signal is used to train the neural networks. The pixels in the low-resolution image are used as the input signal of the neural network to estimate all the wavelet sub-images of the corresponding high-resolution image. The image of increased resolution is finally produced by the synthesis procedure of wavelet transform. In the simulation, the proposed method obtains much better performance than other traditional methods. Moreover, the easy implementation and high flexibility of the proposed algorithm also make it applicable to various other related problems. [ABSTRACT FROM AUTHOR]

A novel neural-network-based method of time series forecasting is presented in this paper. The method combines the optimal partition algorithm (OPA) with the radial basis function (RBF) neural network. OPA for ordered samples is used to perform the clustering for the samples. The centers and widths of the RBF neural network are determined based on the clustering. The difference of the objective functions of the clustering is used to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to stock price prediction. The results of numerical simulations demonstrate the effectiveness of the method. Comparisons with the hard c-means (HCM) algorithm show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the OPA--orthogonal least squares (OPA--OLS) algorithm, which combines OPA with the OLS algorithm, results in better performance for forecasting trends. [ABSTRACT FROM AUTHOR]

Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets. [ABSTRACT FROM AUTHOR]

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths. [ABSTRACT FROM AUTHOR]

The most useful property of neural networks for system identification and control is their ability to approximate arbitrary nonlinear mappings. In this paper, a new neural network model-following control method is proposed for nonlinear servo systems. First, three-layer artificial neural net works are trained to describe the forward and in verse dynamic characteristics of one type of speci fied nonlinear servo systems by using the backpropagation algorithm. Then the neural net work inverse model is used as a feedforward con troller for model-following control of the nonlin ear servo systems. Computer simulation results obtained from MATLAB verify the applicability of neural network identification and control for nonlinear servo systems. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a neural network algorithm that uses the expanded maximum neuron model to solve the channel assignment problem of cellular radio networks, which is an NP-complete combinatorial optimization problem. The channel assignment problem demands minimizing the total interference between the assigned channels needed to satisfy all of the communication needs. The proposed expanded maximum neuron model selects multiple neurons in descending order from the neuron inputs in each neuron group. As a result, the constraints will always be satisfied for the channel assignment problem. To improve the accuracy of the solution, neuron fixing, which is a heuristic technique used in the binary neuron model, a hill-climbing term, a shaking term, and an Omega function are introduced. The effectiveness of these additions to the expanded maximum neuron model algorithm is demonstrated. Simulations of benchmark problems demonstrate the superior performance of the proposed algorithm over conventional algorithms in finding the solution. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(11): 11–19, 2000 [ABSTRACT FROM AUTHOR]

The serious issue in the field of wireless communication is the security and how an organization implements the steps against security breach. The major attack on any organization is Man in the Middle attack which is difficult to manage. This attack leads to number of unauthorized access points, called rogue access points which are not detected easily. In this paper, we proposed a Hopfield Neural Network approach for an automatic detection of these rogue access points in wireless networking. Here, we store the passwords of the authentic devices in the weight matrix format and match the patterns at the time of login. Simulation experiment shows that this method is more secure than the traditional one in WLAN. [ABSTRACT FROM AUTHOR]

As networks expand in area and scale, there arise needs for high-speed, optimal path-design algorithms that can take into account a number of factors. This paper proposes an optimal path-design algorithm using a neural network. The proposed algorithm can be implemented by hardware digital logic circuits, and enables optimal path design to be performed 104 to 106 times faster than the conventional polynomial-time algorithms. The proposed method is also easily implemented in LSI. Another feature of the proposed method is the use of directed and weighted synaptic connections. © 1998 Scripta Technica, Electron Comm Jpn Pt 1, 81(11): 67–76, 1998 [ABSTRACT FROM AUTHOR]

In a multihop radio network, packets are transmitted from course nodes to destination nodes by activating several links between nodes. Each node can either send a packet to, or receive a packet from, at most one of its adjacent nodes simultaneously. To minimize the transmission time for given requests, the problems must be solved by selecting a transmission route for each request (the routing problem) and by finding a link activation schedule (the link activation problem). The routing problem is decomposed into two subproblems: the candidate extraction problem and the route selection problem. In this paper, we propose a neural network algorithm using the maximum neuron model for the route selection problem. We verify through simulations that our algorithm finds better solutions in a shorter time than the existing algorithms. We also probe the NP-hardness of this problem. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 78–86, 1999 [ABSTRACT FROM AUTHOR]

Various studies on learning rules for neural networks have been done. However, most of those do not consider the hardware implementation, which is a great drawback in the LSI implementation of neural networks with learning capability. From such a viewpoint, this paper proposes a self-organizing learning rule by modifying the Hebbian learning rule. This rule can be implemented easily on an analog VLSI chip as an on-chip learning rule. The self-organizing ability of the system is verified by simulation experiments. It is shown from the experiment that the learning speed is improved by a factor of 2 to 3, and it is possible to avoid the sudden termination of the learning and the divergence of the synaptic weights. [ABSTRACT FROM AUTHOR]

This paper proposes a method for learning the process of curve fitting through a general class of totally positive rational bases. The approximation is achieved by finding suitable weights and control points to fit the given set of data points using a neural network and a training algorithm, called AdaMax algorithm, which is a first-order gradient-based stochastic optimization. The neural network presented in this paper is novel and based on a recent generalization of rational curves which inherit geometric properties and algorithms of the traditional rational Bézier curves. The neural network has been applied to different kinds of datasets and it has been compared with the traditional least-squares method to test its performance. The obtained results show that our method can generate a satisfactory approximation. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for the synthesis of neural networks to control gyrostabilizer. The neural network performs the role of a state vector observer. The role of such an observer is to provide feedback on gyrostabilizer, which is illustrated in the article. The paper details the issue of specific stage-related peculiarities of classic algorithms: choosing the network architecture, learning the neural network and verifying the results of feedback control. The article presents an optimal configuration of the neural network like memory depth, number of layers and neurons in these layers, and activation layer functions. It also provides data on dynamic systems to improve learning neural network learning. There is also provided an optimal training pattern. [ABSTRACT FROM AUTHOR]

Identifying voltage variations has an important role to mitigate degradation of power quality disturbances due to their negative impact on the equipment. Therefore, fast and accurate detection and identification are required in order to evaluate and activate the protection. In this paper, Root Mean Square (RMS) algorithm combined with artificial neural network is presented in order to detect and identify voltage variation such as sag, swell, undervoltage and overvoltage. Artificial neural network to identification of voltage variation using feed forward neural network because of its simplicity and accurate. In order to model voltage variations required, a generator is required to emulate of voltage variation waveform. It consists of transformer single input multi output, SSR Switch and microcontroller. Moreover, Identification is implemented with microcontroller STM32F47. Varying neuron under hidden layer is tested to validate the proposed algorithm. The results show that the proposed algorithm is effective to identify each variation with the average percent of error 2.3749% for 8 neurons in the first layer and 0.0658% for 10 neurons in the first layer. [ABSTRACT FROM AUTHOR]

The parameters of the heat source model have significant influence on the temperature field and sequentially affect the residual stress field. In this paper, a neural-network programme based on the Levenberg–Marquardt algorithm is developed to predict the parameters of Goldak's double-ellipsoidal heat source model. The analytical solution of the heat conduction equation based on the double-ellipsoidal heat source is obtained by integrating a series of instant point heat sources over the volume of the ellipsoidal heat source. The transient temperature distribution and the sizes of the molten pool are obtained under various welding processes by using the analytical method. Then, a neural-network programme is employed to train and predict the heat source parameters. These results of temperature and molten pool size obtained by the numerical simulation with the predicted heat source parameters are calibrated by the published experimental results. The numerical results show a good agreement with the experimental measurements. Finally, the developed Levenberg–Marquardt neural network is employed to predict the heat source parameters in the multi-pass welding process in the laboratory. By comparing the finite element (FE) numerical results with experimental results, the heat source parameters have been successfully identified in the multi-pass welding process. [ABSTRACT FROM AUTHOR]

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new ε-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to ε within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with ε-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Networks (RBFNs) are nowadays quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. Performance of the RBF network depends on numerous factors related to its initialization and training. The paper proposes an approach to the radial basis function networks design, where initial parameters of the network, output weights and parameters of the transfer function are set using the proposed agent-based population learning algorithm (PLA). The algorithm is validated experimentally. Advantages and main features of the PLA-based RBF designs are discussed basing on results of the computational experiment. [ABSTRACT FROM AUTHOR]

To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Back-Propagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN "doubly homomorphic" encryption algorithm for the multiparty setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient, and accurate. [ABSTRACT FROM AUTHOR]

In this paper, Levenberg-Marquardt feedforward MLP neural network (LMFFNN) was proposed to classify cervical cell images obtained from 100 patients including healthy, low-grade intraepithelial squamous lesion and high-grade intraepithelial squamous lesion cases. This neural network along with extracted cell image features is a new model for cervical cell image classification. The semiautomated cervical cancer diagnosis system is composed of two phases: image preprocessing/processing and feedforward MLP neural network. In the first stage, image preprocessing is done to reduce the existing noises without lowering the resolution. After that, image processing algorithms were applied to manually cropped cell images to achieve a linear plot which includes real components, were used as LMFFNN inputs for classification of cervical cell images. Based on the results, cervical cell images were classified successfully with 100 % correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100 % using LMFFNN method. It was shown there was a good agreement between the expert decision and values gained from the ANN model. [ABSTRACT FROM AUTHOR]

This paper presents a new inversion approach to estimate the depth profile of a fatigue crack from the output signal of an alternating current field measurements (ACFM) probe. We first show that the mapping between the ACFM signals and corresponding crack depth profiles can be expressed by an affine transformation, then we utilize a generalized version of the fuzzy alignment algorithm (FAA) to find this mapping more efficiently. The proposed method is then extended using the Dave's objective function previously proposed for the fuzzy clustering. To evaluate the accuracy and robustness of the proposed inversion technique we reconstruct cracks of arbitrary shapes from both simulated and measured ACFM signals. The results show that the proposed approach is more robust and accurate than existing methods and gives reasonable results even in the absence of sufficient training data. [ABSTRACT FROM PUBLISHER]

This paper proposes a wavelet-based fuzzy neuro control algorithm for the hazard mitigation of seismically excited buildings equipped with a hybrid control system. The wavelet-based fuzzy neuro controller is developed through the integration of discrete wavelet transform, artificial neural network, and a Takagi-Sugeno fuzzy controller. The hybrid control system is an integrated model of an actuator, a tuned mass damper, and viscous liquid dampers: an active tuned mass damper (ATMD) is located on the top floor of the structure and viscous liquid dampers are located on each floor. To demonstrate the effectiveness of the proposed wavelet-based adaptive neuro-fuzzy inference system (WANFIS) controller, an eight-story building employing passive viscous liquid dampers as well as an ATMD is investigated. A variety of earthquakes such as an artificial earthquake, the 1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes are used as disturbance signals. It is demonstrated that the WANFIS controller is effective in reducing the structural responses of the hybrid structure system subjected to a variety of disturbances. [ABSTRACT FROM AUTHOR]

The objective of this paper is to review some important progress with H infinity control methods and to derive an artificial algorithm for dealing with stability problems of multiple time-delay structural systems. Neural network modeling and linear deferential inclusion are utilized to represent a multiple time-delay structural system. We can derive a novel stability criterion using linear matrix inequality methods. Moreover, the H∞ control performance is ensured for multiple time-delay structural systems based on traditional Lyapunov theory. [ABSTRACT FROM PUBLISHER]

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals. [ABSTRACT FROM AUTHOR]