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As the airline industry is ever expanding, companies are increasing their fleet sizes to obtain greater market shares. Moreover, as the airlines seek more growth, the complexity and size of the airline crew scheduling problem, which is one of the major planning problems in the industry, is also increasing. For this reason, companies dedicate resources to solve this problem, and lease software at great expense from external sources. Rigorous mathematical models and algorithms are used in solving these problems. This paper presents a survey of airline crew scheduling problems, and their proposed solutions from the literature. As a conclusion, prospective studies will be proposed and discussed with the aim of developing better solutions for airline crew scheduling problems in the future. [ABSTRACT FROM AUTHOR]

Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA — it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters. [ABSTRACT FROM AUTHOR]

Imbalanced problem occurs when the size of one class, i.e. the minority class, is much lower than that of the other classes, i.e. the majority classes. Conventional data level methods are employed as the preprocessing approaches to balance the datasets before the classifier learning. Since the balanced data remains unchanged during the learning process, one pre-deleted sample would never be used to train the classifier, which may result in information loss. To solve this problem, this work presents an One-sided Dynamic Undersampling (ODU) technique which adopts all samples in the training process, and dynamically determines whether a majority sample should be used for the classifier learning. Thus, ODU can dynamically undersample the majority class to balance the dataset. To validate the effectiveness of ODU, we integrate it into No-Propagation neural networks to propose an ODU No-Propagation Neural Networks (ODUNPNN). ODUNPNN takes all training samples into consideration, and dynamically undersamples majority class after each iteration, i.e. ODUNPNN integrates undersampling approach into the classifier learning process. Experimental results on both synthetic and real-world imbalance datasets demonstrate that ODUNPNN outperforms the NPNN-based algorithms, and results in comparative performance compared with LASVM-AL, EasyEnsemble, and DyS on real-world imbalance datasets. The contributions of this paper are: (1) ODUNPNN integrates undersampling approach into the classifier learning process. (2) ODUNPNN dynamically balances training data in each iteration. (3) ODU technique can be integrated into other classification learning machines. [ABSTRACT FROM AUTHOR]

Abstract: Forest fire occurrence prediction plays a major role in resource allocation, mitigation and recovery efforts. This paper compares two artificial intelligence based methods, artificial neural networks (ANN) and support vector machines (SVM), utilizing a reduced set of weather parameters. Using a reduced set of parameters results in an efficient and reduced cost prediction system especially for developing countries. In this paper the aim is to predict forest fire occurrence by reducing the number of monitored features, and eliminating the need for weather prediction mechanisms. The reason is to reduce errors due to inaccuracies in weather prediction. The challenge is to choose a limited number of easily measurable features in the aim of reducing the cost of the system and its maintenance. At the same time, the chosen features must have a high correlation with the risk of fire occurrence. A literature review of forest fire prediction methods divided into systems/indices, and artificial intelligence is provided. The two fire danger prediction algorithms utilize relative humidity and cumulative precipitation to output a risk estimate. The assessment of these algorithms, using data from Lebanon, demonstrated their ability to accurately predict the risk of fire occurrence on a scale of four levels. [Copyright &y& Elsevier]

Abstract: The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real world. In this paper I develop a novel algorithm using Two Threshold Sequential Algorithmic Scheme (TTSAS) algorithm and pattern recognition to identify the modulation types of the communication signals automatically. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. In this paper, modulation classification is performed using constellation of the received signal by fuzzy clustering and consequently hierarchical clustering algorithms are used for classification of Quadrature–Amplitude Modulation (QAM) and Phase Shift Keying (PSK) modulations and also modulated signal symbols constellation utilizing TTSAS clustering algorithm, and matching with standard templates, is used for classification of QAM modulation. TTSAS algorithm used here is implemented by the Hamming neural network. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise. [Copyright &y& Elsevier]

Abstract: This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using an Evolutionary Algorithm. Only the latest module added to the network is trained, the previous structure remains fixed. The algorithm allows information from different data domains to be integrated into the network and because the search space in each iteration is small, large and complex networks with a modular structure can emerge naturally. The paper describes an application of the algorithm to a legged robot and discusses its biological inspiration. [Copyright &y& Elsevier]

This paper proposes a new passive robust fault detection scheme using non-linear models that include parameter uncertainty. The non-linear model considered here is described by a group method of data handling (GMDH) neural network. The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed by checking if the measured behaviour is inside the region of possible behaviours based on the so-called forward test since it bounds the direct image of an interval function. The main contribution of this paper is to propose a new backward test, based on the inverse image of an interval function, that allows checking if there exists a parameter in the uncertain parameter set that is consistent with the measured system behaviour. This test is implemented using interval constraint satisfaction algorithms which can perform efficiently in deciding if the measured system state is consistent with the GMDH model and its associated uncertainty. Finally, this approach is tested on the servoactuator being a FDI benchmark in the European Project DAMADICS. [Copyright &y& Elsevier]

Population-based optimization algorithms have been successfully applied to hydrological forecasting recently owing to their powerful ability of global optimization. This paper investigates three algorithms, i.e. differential evolution (DE), artificial bee colony (ABC) and ant colony optimization (ACO), to determine the optimal one for forecasting downstream river flow. A hybrid neural network (HNN) model, which incorporates fuzzy pattern-recognition and a continuity equation into the artificial neural network, is proposed to forecast downstream river flow based on upstream river flows and areal precipitation. The optimization algorithm is employed to determine the premise parameters of the HNN model. Daily data from the Altamaha River basin of Georgia is applied in the forecasting analysis. Discussions on the forecasting performances, convergence speed and stability of various algorithms are presented. For completeness׳ sake, particle swarm optimization (PSO) is included as a benchmark case for the comparison of forecasting performances. Results show that the DE algorithm attains the best performance in generalization and forecasting. The forecasting accuracy of the DE algorithm is comparable to that of the PSO, and yet presents weak superiority over the ABC and ACO. The Diebold–Mariano (DM) test indicates that each pair of algorithms has no difference under the null hypothesis of equal forecasting accuracy. The DE and ACO algorithms are both favorable for searching parameters of the HNN model, including the recession coefficient and initial storage. Further analysis reveals the drawback of slow convergence and time-consumption of the ABC algorithm. The three algorithms present stability and reliability with respect to their control parameters on the whole. It can be concluded that the DE and ACO algorithms are considerably more adaptive in optimizing the forecasting problem for the HNN model. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM), a simple single-hidden-layer feed-forward neural network with fast implementation, has been successfully applied in many fields. This paper proposes an ELM with a constructional structure (CS-ELM) for improving the performance of ELM in dealing with regression problems. In the CS-ELM, there are some partial input subnets (PISs). The first step in designing the PISs is to divide the data-attribute-space into several sub-spaces through using an improved extension clustering algorithm (IECA). The input data attributes in the same sub-space can build a PIS and the similar information of the data attributes is stored in the corresponding PIS. Additionally, a double parallel structure is applied in the CS-ELM, in which there is a special channel that directly connects the input layer neurons to the output layer neurons. In this regard, the proposed procedure can be called ELM with a data-attribute-space-oriented double parallel (DASODP) structure (DASODP–ELM). To test the validity of the proposed method, it is applied to 4 regression applications. The experimental results indicate that, compared with ELM, DASODP–ELM with less number of parameters can achieve higher regression precision in the generalization phase. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks have been successfully used to model nonlinear dynamic systems. However, when a static neural network model is used in system fault detection and the model prediction error is used as the residual, the residual is insensitive to the fault if the neural network used is in dependent mode. This paper proposed the use of a radial basis function network in independent mode as the system model for fault detection, and it was found that the residual is sensitive to the fault. To enhance the signal to noise ratio of the detection the recursive orthogonal least squares algorithm is employed to train the network weights. Another radial basis function network is used to isolate fault using the information in the residual signal. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at the Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations. [Copyright &y& Elsevier]

In this paper, a hybrid intelligent tracking control approach is developed to address optimal tracking problems for a class of nonlinear discrete-time systems. The generalized value iteration algorithm is utilized to attain the admissible tracking control with off-line training, while the on-line near-optimal control method is established to enhance the control performance. It is emphasized that the value iteration performance is improved by introducing the acceleration factor. By collecting the input–output data of the unknown system plant, the model neural network is constructed to provide the partial derivative of the system state with respect to the control law as the approximate control matrix. A novel computational strategy is introduced to obtain the steady control of the reference trajectory. The critic and action neural networks are utilized to approximate the cost function and the tracking control, respectively. Considering approximation errors of neural networks, the stability analysis of the specific systems is provided via the Lyapunov approach. Finally, two numerical examples with industrial application backgrounds are involved for verifying the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: In a competitive electricity market, the forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. In this paper, a new forecast strategy is proposed for day ahead prediction of electricity price, which is a complex signal with nonlinear, volatile and time dependent behavior. Our forecast strategy includes a new two stage feature selection algorithm, a composite neural network (CNN) and a few auxiliary predictors. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information (MI) criterion and selects the input variables of the CNN among a large set of candidate inputs. The CNN is composed of a few neural networks (NN) with a new data flow among its building blocks. The CNN is the forecast engine of the proposed strategy. A kind of cross-validation technique is also presented to fine-tune the adjustable parameters of the feature selection algorithm and CNN. Moreover, the proposed price forecast strategy is equipped with a few auxiliary predictors to enrich the candidate set of inputs of the forecast engine. The whole proposed strategy is examined on the PJM, Spanish and Californian electricity markets and compared with some of the most recent price forecast methods. [Copyright &y& Elsevier]

Abstract: The management of atmospheric pollution using video is not yet widespread. However it is an efficient way to evaluate the polluting rejects coming from large industrial facilities when traditional captors are not usable. This paper presents a comparison of different classifiers for a monitoring system of polluting smokes. The data used in this work are stemming from a system of video analysis and signal processing. The database includes the pollution level of puffs of smoke defined by an expert. Six machine learning techniques are tested and compared to classify the puffs of smoke: k-nearest neighbour, naïve Bayes classifier, artificial neural network, decision tree, support vector machine and a fuzzy model. The parameters of each type of classifier are split into three categories: learned parameters, parameters determined by a first step of the experimentation, and parameters set by the programmer. We compare the results of the best classifier of each type depending on the size of the learning set. A part of the discussion concerns the robustness of the classifier facing the case where classes of interest are under-represented, as the high level of pollution in our data. [Copyright &y& Elsevier]

Abstract: Uncertain ground conditions represent the primary source of risk in underground tunnel construction. However, this problem can be solved by developing an accurate, probabilistic description of the geology. This paper presents a general model for probability based determination of tunnel geology that can be used as a basis for developing more effective decision support systems for tunneling design and construction. The proposed model is based on a Hidden Markov Model (HMM) and a neural network (NN). An approximate inference technique – a Particle Filter (PF) Algorithm – is used to simulate the geological parameters. This model overcomes the deficiencies of existing models by readily incorporating all available geologic information and updating geologic predictions based on observations given by the neural network. In order to validate the proposed model, the “Drainage Water Tunnel Project” at Zhong-He, Taipei, Taiwan was used. The results showed that the Neural-HMM model provides high accuracy in geological prediction. [Copyright &y& Elsevier]

Abstract: Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence. Method: Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function. Results: The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers. [Copyright &y& Elsevier]

Abstract: Nonlinear model predictive control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for output-feedback NMPC based on various black-box models can be found in the literature. However, NMPC involving on-line optimization is computationally very demanding. On the other hand, an explicit solution to the NMPC problem would allow efficient on-line computations as well as verifiability of the implementation. This paper applies an approximate multi-parametric nonlinear programming approach to explicitly solve output-feedback NMPC problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors. The approach is applied to design an explicit NMPC for regulation of a pH maintaining system. The verification of the NMPC controller performance is based on simulation experiments. [ABSTRACT FROM AUTHOR]

Abstract: The behaviour of the TCP AIMD algorithm is known to cause queue length oscillations when congestion occurs at a router output link. Indeed, due to these queueing variations, end-to-end applications experience large delay jitter. Many studies have proposed efficient active queue management (AQM) mechanisms in order to reduce queue oscillations and stabilize the queue length. These AQM attempt to improve the random early detection (RED) model. Unfortunately, these enhancements do not react in a similar manner for various network conditions and are strongly sensitive to their initial setting parameters. Although this paper proposes a solution to overcome the difficulties of configuring the RED parameters by using a Kohonen neural network model; another goal of this study is to investigate whether cognitive intelligence could be placed in the core network to solve such stability problem. In our context, we use results from the neural network area to demonstrate that our proposal, named Kohonen-RED (KRED), enables a stable queue length without complex parameters setting or passive measurements to obtain a correct configuration. [ABSTRACT FROM AUTHOR]

Abstract: Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions. [Copyright &y& Elsevier]

Abstract: This paper proposes an intelligent task planning and action selection mechanism for a mobile robot in a robot soccer system through a fuzzy neural network approach. The proposed fuzzy neural network system is developed through the two dimensional fuzzification of the soccer field. A five layer fuzzy neural network system is trained through error back propagation learning algorithm to impart a strategy based action selection. The action selection depends on the field configuration, and the emergence of a particular field configuration results from the game dynamics. Strategy of the robot changes when the configuration of the objects in the field changes. The proposed fuzzy neural network structure is flexible to accommodate all possible filed configurations. Simulation results indicate that the proposed approach is simple and has the capability in coordinating the multi-agent system through selection of sensible actions. [Copyright &y& Elsevier]

Abstract: The paper presents an algorithm of idle speed stabilization in the spark ignition automotive engine by means of spark advance control. The algorithm is based on a well-known approach of a model-based adaptive control and uses artificial neural networks. The control algorithm is based on a neural network model observer of the additional effective torque. The additional load is estimated as difference between effective torque, estimated by the neural network observer, and brake torque, estimated on the basis of a linear quadratic model. In that case the additional load is understood as the sum of the alternator brake torque (additional load form electric car equipments) and the momentary and/or permanent changes of the engine’s characteristics. On the basis of estimated values of the additional load, the required value of angular acceleration is determined to make the engine return to the specified speed. This acceleration is achieved by adjusting the spark advance. The required value of spark advance is estimated by means of a neural network model converse to that of the effective torque. The algorithm was experimentally compared with PID and adaptive algorithms in the same test bed. The tests were conducted under sudden change of external load. The proposed algorithm proved to be more effective in terms of control error. [Copyright &y& Elsevier]

Abstract: In this paper, we are considering that the design process can be modelled in the form of a constraint satisfaction problem (CSP). CSP modelling or resolution has proved its efficiency within the framework of single-designer design. We propose to extend the functions of CSP to the context of multi-concept design of the same artefact. We define CoCSP as cooperative constraint satisfaction problem including the actors of the design problem. We are presenting the operating principles of an algorithm for the real-time management of design decisions, based on a model described in the form of a CoCSP for the integration of supply-chain constraints. This algorithm enables the number of design decisions rejected at a given moment in design to be kept to a minimum. The algorithm forms the core of a prototype for an unsupervised, generic constraint-based collaborative design system. Our aim is to produce a platform centred on the notion of constraints that will enable a product design problem to be modelled and solved by integrating supply-chain constraints as far upstream as possible. [Copyright &y& Elsevier]

Abstract: The recent product traceability requirements, particularly in food production chains, demonstrate an industrial need to improve traceability systems. Having real-time access to traceability information allows its exploitation, which is the aim of this work. In this paper, the problem of minimizing the cost of products recall is treated. First the raw material dispersion problem is analyzed, in order to determine a risk-level criterion or “production criticality”. This criterion is used subsequently to optimize deliveries dispatch with the purpose of minimizing the number of batch recalls in case of crisis. This is achieved by implementing decision-making aid tools based on operational research and artificial intelligence. [Copyright &y& Elsevier]

Abstract: Automotive engines are multivariable system with severe non-linear dynamics, and their modelling and control are challenging tasks for control engineers. Current control of engine used look-up table combined with proportional and integral (PI) control and is not robust to system uncertainty and time varying effects. In this paper the model predictive control strategy is applied to engine air/fuel ratio control using neural network model. The neural network model uses information from multivariables and considers engine dynamics to do multi-step ahead prediction. The model is adapted in on-line mode to cope with system uncertainty and time varying effects. Thus, the control performance is more accurate and robust compared with non-adaptive model based methods. To speed up algorithm calculation, different optimisation algorithms are investigated and performance compared. Finally, the developed method is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results demonstrate the effectiveness of the developed method. [Copyright &y& Elsevier]

The paper focuses on developing the system for people flow recognition based on many video camera images. The project arose based on the real need — its application in the city malls to find the most visited places and support customers' campaigns. The potential application in other domains is possible (public buildings, airports). In contrast to the existing solutions, our approach involves on-edge image analysis to decrease the risk of data loss and the system cost. In the project, we design the whole processing pipeline composed of the component modules responsible for detecting, tracking, and reidentifying (shuffling) people. Our experimental platform enabled us to compare multiple method variants for each module. Based on extensive experimental research, the final solution uses the pretrained SSD method in the detection module. The centroid algorithm applied to displacement vectors combined with the Siamese network is the basis for the object tracking module. The best model to solve the reidentification task is the Resnet50. For the Market 1501 dataset, it achieved Rank-1 efficiency of 84.6%. The system gives a visualization of the main paths of people's movements in the form of a heat map and assigns the direction where people most often look. In the experimental study, we assessed the system's effectiveness and time efficiency, and the current results give a perspective for its commercialization in the nearest future. • Development of the whole processing pipeline of human flow and reidentification. • Research aiming at the choice of competitive component methods • Experimental tuning of methods to achieve the high efficiency and low processing time. • Extension of the centroids algorithm with displacement vectors. • Platform for easy testing various methods of detection and reidentification. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an identification method based on artificial neural networks, which can be used for the robust fault detection. In particular, a problem of the multi-layer perceptron neural network uncertainty estimation with application of the outer bounding ellipsoid algorithm is considered. The mathematical description of the model uncertainty enables designing robust fault detection system, which effectiveness was verified with the DAMADICS benchmark. [Copyright &y& Elsevier]

Abstract: Digital signal-type identification is an important issue in communication intelligence system. Most of the proposed identifiers (techniques) can only identify a few kinds of digital signal and/or low order of digital signals. They usually require high levels of signal-to-noise ratio (SNR). This paper presents an intelligent technique that includes a variety of digital signals. In this technique, a combination of the higher-order moments and the higher-order cumulants up to eighth are proposed as the effective features for representation of the considered digital signals. A multilayer perceptron neural network with resilient back propagation learning algorithm is utilized to determine class of the received signal. The numbers of nodes in the hidden layer, along with the selection of input features are optimized using a genetic algorithm. Simulation results show that the proposed technique has high performance for identification the different digital signal types even at very low SNRs. This high performance is achieved with only seven selected features and the least possible number of nodes in the hidden layer, which have been optimized using the genetic algorithm. [Copyright &y& Elsevier]

Abstract: The paper investigates principles of work and BER characteristics of a new soft decision algorithm for decoding convolutional codes that is based on neural network applications. The novelty of the algorithm is in its capability to generate soft output estimates of the message bits encoded. For this purpose the noise energy function, which is defined and used for the neural network decoding of convolutional codes, has been related to the well known log-likelihood function and the soft decision decoding rule has been defined and derived. The BER curves are obtained for this novel algorithm and then compared to the curve obtained by the Viterbi algorithm and the gradient descent algorithm. Based on the theoretical model a simulator of coding communication system has been developed and used to confirm theoretically expected results. It was found that the performances of the proposed soft decision decoder are comparable or better than the performances of the recurrent neural network decoder and decoders based on the Viterbi algorithm. [Copyright &y& Elsevier]

Abstract: This paper proposes an integrated system for the binarization of normal and degraded printed documents for the purpose of visualization and recognition of text characters. In degraded documents, where considerable background noise or variation in contrast and illumination exists, there are many pixels that cannot be easily classified as foreground or background pixels. For this reason, it is necessary to perform document binarization by combining and taking into account the results of a set of binarization techniques, especially for document pixels that have high vagueness. The proposed binarization technique takes advantage of the benefits of a set of selected binarization algorithms by combining their results using a Kohonen self-organizing map neural network. Specifically, in the first stage the best parameter values for each independent binarization technique are estimated. In the second stage and in order to take advantage of the binarization information given by the independent techniques, the neural network is fed by the binarization results obtained by those techniques using their estimated best parameter values. This procedure is adaptive because the estimation of the best parameter values depends on the content of images. The proposed binarization technique is extensively tested with a variety of degraded document images. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented. [Copyright &y& Elsevier]

Abstract: In this paper a new iterative construction algorithm for local model networks is presented. The algorithm is focussed on building models with sparsely distributed data as they occur in engine optimization processes. The validity function of each local model is fitted to the available data using statistical criteria along with regularization and thus allowing an arbitrary orientation and extent in the input space. Local models are consecutively placed into those regions of the input space where the model error is still large thus guaranteeing maximal improvement through each new local model. The orientation and extent of each validity function are also adapted to the available training data such that the determination of the local regression parameters is a well-posed problem. The regularization of the model can be controlled in a distinct manner using only two user-defined parameters. In order to assess the quality of the obtained model, the algorithm also provides accurate model statistics. Different examples illustrate the efficiency of the proposed algorithm. One illustrative example shows how local models are adapted to the shape of the target function in the presence of noise. A second example shows results obtained with measurement databases of IC-engines. [Copyright &y& Elsevier]

Abstract: The estimation of the aerobic phase end-point is usually used to improve the operating capacity in a sequencing batch reactor. In this paper, a software tool and a configuration of the dissolved oxygen control closed loop are proposed to achieve the aerobic end-point detection of a sequencing batch reactor in a coke wastewater treatment plant. The proposed software tool consists of self-organizing map (SOM) and clustering algorithms. Moreover a validation method for SOM training is outlined and a predefined criterion to determine the SOM size is tested. [Copyright &y& Elsevier]

Abstract: Radial basis function neural networks (RBFNs) can be applied to model the I–V characteristics and maximum power points (MPPs) of photovoltaic (PV) panels. The key issue for training an RBFN lies in determining the number of radial basis functions (RBFs) in the hidden layer. This paper presents a genetic algorithms-based RBFN training scheme to search for the optimal number of RBFs using only the input samples of a PV panel. The performance of the trained RBFN is comparable with that of the conventional model and the training algorithm is computationally efficient. The trained RBFNs have been applied to predict MPPs of two different practical PV panels. The results obtained are accurate enough for applying the models to control the PV systems for tracking the optimal power points. [Copyright &y& Elsevier]

Abstract: Neural networks have recently received a great deal of attention in the field of manufacturing process quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a manufacturing process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for manufacturing quality monitoring. [Copyright &y& Elsevier]

Abstract: Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter uncertainties. In this paper, to overcome this problem, a neuro fuzzy controller (NFC) is proposed. Heuristic rules are derived with the membership functions of the fuzzy variables tuned by a neural network (NN). The algorithm is implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the NFC with various working conditions of the SRM. [Copyright &y& Elsevier]

Abstract: Globally linearizing control (GLC) is a control algorithm capable of using nonlinear process model directly. In GLC, mostly, first-principle models derived from dynamic mass, energy and momentum balances are used. When the process is not perfectly known, the unknown parts of the first principles model should be represented by black-box models, e.g. by neural networks. This paper is devoted to the identification and application of such hybrid models for GLC. It is shown that the first principles part of the model determines the dominant structure of the controller, while the black-box elements of the hybrid model are used as state and/or disturbance estimators. For the identification of the neural network elements of the hybrid model a sensitivity approach based algorithm has been developed. The underlying framework is illustrated by the temperature control of a continuous stirred tank reactor where a neural network is used to model the heat released by an exothermic chemical reaction. [Copyright &y& Elsevier]

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed. [Copyright &y& Elsevier]

Wheeled or tracked vehicles cannot move easily over much of the land surface of the earth. This paper describes research work to create walking machines that are able to travel when the terrain makes wheeled or tracked vehicles ineffective. These legged walking vehicles must be able to negotiate unknown environments with little or no knowledge of the terrain. A predictive terrain contour mapping strategy is proposed that uses a feed-forward neural network trained using a back-propagation algorithm to predict contours based on leg positions and orientations. The strategy is tested using the abilities of a tele-operated eight-legged robot named “Robug IV”. Predicted performance is an improvement on previous implementations and a summarised comparison of the results for the four terrains is provided. [Copyright &y& Elsevier]