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The geometric measure of entanglement of a multipartite pure state is defined it terms of its geometric distance from the set of separable pure states. The quantum eigenvalue problem is derived to compute the separable pure state nearest to the given multipartite pure state. Computing the modulus largest quantum eigenvalue for a multipartite pure state is equivalent to finding the best complex rank-one approximation of the complex unit tensors, associated with the multipartite pure states. This paper is devoted to present a complex-valued neural networks approach for the computation of the quantum eigenvalue problem for multipartite pure states. We design the neural networks for computing the best rank-one tensor approximation of complex tensors, and prove that the solution of the networks is locally asymptotically stable in the sense of Lyapunov stability theory. This solution also converges to the local optimal solutions of the best complex rank-one tensor approximation. We illustrate our theoretical results via numerical simulations. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a global Gaussian mixture (GM) reduction algorithm via clustering for extended target tracking in clutter. The proposed global clustering algorithm is obtained by combining a fuzzy Adaptive Resonance Theory (ART) neural network architecture with the weighted Kullback–Leibler (KL) difference which describes discrimination of one component from another. Therefore, we call the proposed algorithm as ART-KL clustering (ART-KL-C) in the paper. The weighted KL difference is used as a category choice function of ART-KL-C, derived by considering both the KL divergence between two components and their weights. The performance of ART-KL-C is evaluated by the normalized integrated squared distance (NISD) measure, which describes the deviation between the original and reduced GM. The proposed algorithm is tested on both one-dimensional and four-dimensional simulation examples, and the results show that the proposed algorithm can more accurately approximate the original mixture and is useful in extended target tracking. [Copyright &y& Elsevier]

Abstract: In this paper, the issue of nonlinear sensitivity analysis for dimensionality reduction in hydrologic model calibration is discussed, and a novel method to quantify the sensitivity of each parameter that considers the nonlinear relationship in the model is presented. The method is based on computing the absolute variation of the nonlinear function represented by the model in its parameter space. The paper discusses the theoretical background of the method and presents the algorithm. The algorithm employs neural network as a pseudo simulator to reduce the computational burden of the analysis. The proposed approach of sensitivity analysis is illustrated through a case study on a physically based distributed hydrologic model. The results indicate that the method is able to rank the parameters effectively, and the ranking can be interpreted in the context of the physical processes being considered by the model. [ABSTRACT FROM AUTHOR]

Abstract: The focus of this paper is to propose a hybrid neural network model for associative recall of analog and digital patterns. This hybrid model consists of self-feedback neural network structures (SFNN) in parallel with generalized regression neural networks (GRNN). Using a new one-shot learning algorithm developed in the paper, pattern representations are first stored as the asymptotically stable fixed points of the SFNN. Then in the retrieving process, each pattern is applied to the GRNN to make the corresponding initial condition and to initiate the dynamical equations of the SFNN that should in turn output the corresponding representation. In this way, the corresponding stored patterns are retrieved even under high noise degradation. Moreover, contrary to many associative memories, the proposed hybrid model is without any spurious attractors and can store both binary and real-value patterns without any preprocessing. Several simulations confirm the theoretical analyses of the model. Results indicate that the performance of the hybrid model is better than that of recurrent associative memory and competitive with other classes of networks. [Copyright &y& Elsevier]

Abstract: In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm. [Copyright &y& Elsevier]

This paper focuses on studying the H ∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov–Krasovskii functional. By constructing a suitable augmented Lyapunov–Krasovskii functional with triple integral terms and linear matrix inequality technique, the delay-dependent criteria are conferred so that the error system is globally asymptotically stable with H ∞ performance. The activation functions are assumed to satisfy sector-like nonlinearities. The desired estimator gain matrix can be characterized in terms of the solution to linear matrix inequalities, which can be easily solved by some standard numerical algorithms. Numerical simulation is given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results. [ABSTRACT FROM AUTHOR]

Abstract: Brand choice models, used for describing the process of choosing mutually exclusive alternatives, attracted a large amount of attention from researchers in the early of the recent decade. These models can be used as a market response simulator for simulating marketing strategies and assessing how changes in marketing variables such as pricing and promotions will influence consumer behavior and thus perform ‘what-if’ simulations. So a reliable and relevant brand choice model can be very useful and effective, which, in fact, represents a worthwhile opportunity to improve the efficiency of the marketing decisions. In this paper we offer a new approach by integrating of Probabilistic Neural Network (PNN) and Data preprocessing for brand choice modeling and constructing a market response simulator. This approach, called Preprocessed-Probabilistic Neural Network (PPNN), consists of two main stages. First, a robust Genetic Based Instance Selection Model (GBIS) employed to look for a representative data subset of instances in training data set. The second stage ends up with a relevant brand choice model, using a probabilistic neural network trained by Dynamic Decay Adjustment Algorithm (DDA). The evaluation process is carried out using the same data set been used in literature for modeling individual consumer choices in a retail coffee market. The evaluation results show that the offered approach outperforms all previous methods, so it can be considered as an effective tool for consumer behavior modeling and simulation. [Copyright &y& Elsevier]

Abstract: Techniques employed in the synthesis of antenna arrays vary from complex analytical methods to iterative numerical methods based on optimisation algorithms. The drawback of these techniques is that they usually consider the array factor but not the interaction between array elements and real-time problems. This omission induces an error in the resultant radiation pattern; therefore, the physical relations between the array feeding details and the corresponding radiation patterns are taken into account to improve the accuracy. The behaviour of an antenna array is nonlinear in nature, resulting in an extremely high complexity using this approach, and it is usually disregarded. A neural-network-based solution can avoid complexity by establishing a relation between the desired radiation patterns and feeding details such as voltage and spacing in the real antenna array and can help convert the real array into a smart array. Several neural network applications in smart antenna array synthesis are reviewed in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature. [Copyright &y& Elsevier]

Abstract: A principal component analysis (PCA) neural network is developed for online extraction of the multiple minor directions of an input signal. The neural network can extract the multiple minor directions in parallel by computing the principal directions of the transformed input signal so that the stability-speed problem of directly computing the minor directions can be avoided to a certain extent. On the other hand, the learning algorithms for updating the net weights use constant learning rates. This overcomes the shortcoming of the learning rates approaching zero. In addition, the proposed algorithms are globally convergent so that it is very simple to choose the initial values of the learning parameters. This paper presents the convergence analysis of the proposed algorithms by studying the corresponding deterministic discrete time (DDT) equations. Rigorous mathematical proof is given to prove the global convergence. The theoretical results are further confirmed via simulations. [ABSTRACT FROM AUTHOR]

Abstract: Minor subspace analysis (MSA) is a statistical method for extracting the subspace spanned by all the eigenvectors associated with the minor eigenvalues of the autocorrelation matrix of a high-dimension vector sequence. In this paper, we propose a self-stabilizing neural network learning algorithm for tracking minor subspace in high-dimension data stream. Dynamics of the proposed algorithm are analyzed via a corresponding deterministic continuous time (DCT) system and stochastic discrete time (SDT) system methods. The proposed algorithm provides an efficient online learning for tracking the MS and can track an orthonormal basis of the MS. Computer simulations are carried out to confirm the theoretical results. [Copyright &y& Elsevier]

Abstract: An Earth-to-Air Heat Exchanger (ETAHE) is a low energy cooling and heating building component. It uses the ground''s thermal storage to dampen ambient air temperature oscillations by delivering the air through a horizontally buried duct. To reduce airflow resistance, some hybrid ventilated buildings have recently adopted large cross-sectional area ducts. This paper describes the development of an Artificial Neural Network based Heat Convection (ANN-HC) algorithm to predict local average Nusselt Numbers along the duct surfaces. Furthermore, the ANN-HC algorithm is integrated with a transient three-dimensional heat transfer model based on finite element analysis of heat conduction in the ground domain surrounding the ETAHE to establish a new thermal modeling method for ETAHEs. A case study is presented to demonstrate the working principle of the new method. It is shown that the method can very well simulate the interactions between an ETAHE and its environment. [Copyright &y& Elsevier]

Abstract: In actual nonlinear active noise control (NANC) systems, there often exist nonlinear distortions in such cases: the primary path may be nonlinear, the reference noise may exhibit nonlinear distortion, and the secondary path may have nonminimum-phase. To solve the problems of nonlinear distortions, two novel feedback adaptive filters based on the functional link neural network (FLNN) for NANC systems with low computational complexity are proposed in this paper, which are a feedback functional link neural network (FFLNN) and a reduced feedback functional link neural network (RFFLNN), respectively. To train the proposed nonlinear filters for NANC systems, a reduced complexity filtered-s least mean square (FSLMS) algorithm using filter bank approach is developed. The analysis of computational complexity shows that the RFFLNN adaptive filter involves less computation as compared to FFLNN and FLNN adaptive filters. Moreover, it is demonstrated through computer simulations for nonlinear noise processes that the RFFLNN adaptive filter outperforms FLNN and FFLNN in term of convergence speed and steady-state error. Furthermore, it is more effective in reducing nonlinear effects in NANC systems than other filters. [Copyright &y& Elsevier]

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger¿Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation. [Copyright &y& Elsevier]

Abstract: Neural networks have some applications in computerized tomography, in particular to reconstruct an image from projections. The presented paper describes a new practical approach to the reconstruction problem using a Hopfield-type neural network. The methodology of this reconstruction algorithm resembles a transformation formula—the so-called ρ-filtered layergram method. The method proposed in this work is adapted for discrete fan beam projections, already used in practice. Performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in obtained image quality, and in perspective of hardware implementation in the speed of the reconstruction process. [Copyright &y& Elsevier]

Abstract: A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an assumption could not be held in many advanced applications. Thus, this paper proposes the adaptive neural algorithm (ANA) which designs and associates several auto-adjust mechanisms to challenge these advanced BSS problems. The first implementation is the on-line estimator of source numbers improved from the cross-validation technique. The second is the adaptive structure neural network that combines feed-forward architecture and the self-organized criterion. The last is the learning rate adjustment in order to enhance efficiency of learning. The validity and performance of the proposed algorithm are demonstrated by computer simulations, and are compared to algorithms with state of the art. From the simulation results, these have been confirmed that the proposed ANA performed better separation than others in static BSS cases and is feasible for dynamic BSS cases. [Copyright &y& Elsevier]

Abstract: This paper presents a Wiener-type recurrent neural network with a systematic identification algorithm and a control strategy for the identification and control of unknown dynamic nonlinear systems. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our network include: (1) the two subsystems are integrated into a single network whose output is expressed by a nonlinear transformation of a linear state-space equation; (2) the characteristics of the trained network can be analyzed by its associated state-space equation using the well-developed theory of linear systems; and (3) the size of the network structure is determined by the number of state variables (or the system order) of the unknown systems to be identified. To effectively identify a given unknown system from its input–output data, we have developed a systematic identification algorithm that consists of an order determination procedure, a parameterization procedure, and an online learning procedure. The false nearest neighbors algorithm was adopted to acquire a minimal embedding dimension from the input–output data as the system order, and then the eigensystem realization algorithm (ERA) was used to initialize a best-fit state-space representation according to the acquired system order. To improve the overall identification performance, we have derived an online parameter learning algorithm based on an ordered derivatives and momentum terms. Subsequently, a simple feedback linear controller was designed to control the unknown dynamic nonlinear systems without much complexity. Computer simulations and comparisons with some existing recurrent networks have conducted to confirm the effectiveness and superiority of the proposed Wiener-type network, identification algorithm and control strategy. [Copyright &y& Elsevier]

Abstract: This paper proposes an improved learning algorithm of analog compound orthogonal networks and a novel tracking control approach for nonholonomic mobile robots by integrating the neural network into the backstepping technique. The adaptive control is derived from continuously tuning parameters using the analog neural network in the backstepping control law. The proposed control approach for the mobile robot has the properties to quickly drive the position error to zero and to indicate better smooth movement in the tracking control process. These features are due to continuous online learning and adaptive capability of analog neural networks. Simulation results demonstrate that the proposed control approach is effective and has better control performance. [Copyright &y& Elsevier]

Abstract: Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks. [Copyright &y& Elsevier]

Abstract: A digital spiking neuron is a wired system of shift registers and can generate various spike-trains by adjusting the wiring pattern. In this paper we analyze the basic relations between the wiring pattern and characteristics of the spike-train. Based on the relations, we present a learning algorithm which utilizes successive changes of the wiring pattern. It is shown that the neuron can reproduce spike-trains of another neuron which has an unknown wiring pattern. It is also shown that the neuron can approximate various spike-trains of a chaotic analog spiking neuron. [Copyright &y& Elsevier]

Abstract: This paper proposes blind source extraction methods based on several time-delay autocorrelations of primary sources, called MACBSE. The MACBSE approaches are batch fixed-point learning algorithms for extraction of source signals with linear autocorrelations. The fixed-point algorithms are very simple and do not need to choose any learning step sizes. Furthermore, the convergence properties of the algorithms are analyzed. Their efficiencies are demonstrated by extensive computer simulations. [Copyright &y& Elsevier]

Abstract: A neural approach for the parameter estimation of adaptive FIR filters for linear system identification is presented in the paper. It is based on a linear neuron with a modified gradient algorithm, capable of resolving the total least squares (TLS) problem present in this kind of estimation, where noisy errors affect not only the observation vector but also the data matrix. The learning rule is analyzed mathematically. The results of computer simulations are given to illustrate that the neural approach considerably outperforms the existing TLS methods when a larger learning factor is used or the signal-noise-ratio (SNR) is lower. [Copyright &y& Elsevier]