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Neural Networks: the Official Journal of the International Neural Network Society

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https://www.readbyqxmd.com/read/28646764/adaptive-near-optimal-neuro-controller-for-continuous-time-nonaffine-nonlinear-systems-with-constrained-input
#1
Kasra Esfandiari, Farzaneh Abdollahi, Heidar Ali Talebi
In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28646763/deep-neural-mapping-support-vector-machines
#2
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28600976/master-slave-exponential-synchronization-of-delayed-complex-valued-memristor-based-neural-networks-via-impulsive-control
#3
Xiaofan Li, Jian-An Fang, Huiyuan Li
This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system...
May 25, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28646762/hybrid-impulsive-and-switching-hopfield-neural-networks-with-state-dependent-impulses
#4
Xianxiu Zhang, Chuandong Li, Tingwen Huang
We discuss the global stability of switching Hopfield neural networks (HNN) with state-dependent impulses using B-equivalence method. Under certain conditions, we show that the state-dependent impulsive switching systems can be reduced to the fixed-time ones, and that the global stability of corresponding comparison system implies the same stability of the considered system. On this basis, a novel stability criterion for the considered HNN is established. Finally, two numerical examples are given to demonstrate the effectiveness of our results...
May 24, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28599148/memristor-standard-cellular-neural-networks-computing-in-the-flux-charge-domain
#5
Mauro Di Marco, Mauro Forti, Luca Pancioni
The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the flux-charge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state. (b) The dynamic equations describing the memristor NNs in the flux-charge domain are analogous to those describing, in the traditional voltage-current domain, the dynamics of a standard (S) cellular (C) NN, and are implemented by using a realistic model of memristors as that proposed by HP...
May 24, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28582671/pinning-synchronization-of-memristor-based-neural-networks-with-time-varying-delays
#6
Zhanyu Yang, Biao Luo, Derong Liu, Yueheng Li
In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control...
May 18, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28575737/bayesian-geodesic-path-for-human-motor-control
#7
Ken Takiyama
Despite a near-infinite number of possible movement trajectories, our body movements exhibit certain invariant features across individuals; for example, when grasping a cup, individuals choose an approximately linear path from the hand to the cup. Based on these experimental findings, many researchers have proposed optimization frameworks to determine desired movement trajectories. Successful conventional frameworks include the geodesic path, which considers the geometry of our complicated body dynamics, and stochastic frameworks, which consider movement variability...
May 17, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28575735/fractional-order-leaky-integrate-and-fire-model-with-long-term-memory-and-power-law-dynamics
#8
Wondimu W Teka, Ranjit Kumar Upadhyay, Argha Mondal
Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory...
May 17, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28575736/collective-neurodynamic-optimization-for-economic-emission-dispatch-problem-considering-valve-point-effect-in-microgrid
#9
Tiancai Wang, Xing He, Tingwen Huang, Chuandong Li, Wei Zhang
The economic emission dispatch (EED) problem aims to control generation cost and reduce the impact of waste gas on the environment. It has multiple constraints and nonconvex objectives. To solve it, the collective neurodynamic optimization (CNO) method, which combines heuristic approach and projection neural network (PNN), is attempted to optimize scheduling of an electrical microgrid with ten thermal generators and minimize the plus of generation and emission cost. As the objective function has non-derivative points considering valve point effect (VPE), differential inclusion approach is employed in the PNN model introduced to deal with them...
May 15, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28552508/synchronization-of-stochastic-reaction-diffusion-neural-networks-with-dirichlet-boundary-conditions-and-unbounded-delays
#10
Yin Sheng, Zhigang Zeng
In this paper, synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and unbounded discrete time-varying delays is investigated. By virtue of theories of partial differential equations, inequality methods, and stochastic analysis techniques, pth moment exponential synchronization and almost sure exponential synchronization of the underlying neural networks are developed. The obtained results in this study enhance and generalize some earlier ones. The effectiveness and merits of the theoretical criteria are substantiated by two numerical simulations...
May 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28552507/ordinal-regression-based-on-learning-vector-quantization
#11
Fengzhen Tang, Peter Tiňo
Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted...
May 9, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28552509/nonredundant-sparse-feature-extraction-using-autoencoders-with-receptive-fields-clustering
#12
Babajide O Ayinde, Jacek M Zurada
This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features...
May 6, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28544890/hybrid-neuro-heuristic-methodology-for-simulation-and-control-of-dynamic-systems-over-time-interval
#13
Marcin Woźniak, Dawid Połap
Simulation and positioning are very important aspects of computer aided engineering. To process these two, we can apply traditional methods or intelligent techniques. The difference between them is in the way they process information. In the first case, to simulate an object in a particular state of action, we need to perform an entire process to read values of parameters. It is not very convenient for objects for which simulation takes a long time, i.e. when mathematical calculations are complicated. In the second case, an intelligent solution can efficiently help on devoted way of simulation, which enables us to simulate the object only in a situation that is necessary for a development process...
May 5, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28544891/emergence-of-ultrafast-sparsely-synchronized-rhythms-and-their-responses-to-external-stimuli-in-an-inhomogeneous-small-world-complex-neuronal-network
#14
Sang-Yoon Kim, Woochang Lim
We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons, and investigate the effect of network architecture on emergence of synchronized brain rhythms by varying the fraction of LR interneurons plong. The betweenness centralities of the LR and SR interneurons (characterizing the potentiality in controlling communication between other interneurons) are distinctly different. Hence, in view of the betweenness, SWNs we consider are inhomogeneous, unlike the "canonical" Watts-Strogatz SWN with nearly the same betweenness centralities...
April 28, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28525811/an-online-supervised-learning-method-based-on-gradient-descent-for-spiking-neurons
#15
Yan Xu, Jing Yang, Shuiming Zhong
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses...
April 27, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28500895/a-universal-multilingual-weightless-neural-network-tagger-via-quantitative-linguistics
#16
Hugo C C Carneiro, Carlos E Pedreira, Felipe M G França, Priscila M V Lima
In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks...
April 26, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28494328/robust-stability-analysis-of-quaternion-valued-neural-networks-with-time-delays-and-parameter-uncertainties
#17
Xiaofeng Chen, Zhongshan Li, Qiankun Song, Jin Hu, Yuanshun Tan
This paper addresses the problem of robust stability for quaternion-valued neural networks (QVNNs) with leakage delay, discrete delay and parameter uncertainties. Based on Homeomorphic mapping theorem and Lyapunov theorem, via modulus inequality technique of quaternions, some sufficient conditions on the existence, uniqueness, and global robust stability of the equilibrium point are derived for the delayed QVNNs with parameter uncertainties. Furthermore, as direct applications of these results, several sufficient conditions are obtained for checking the global robust stability of QVNNs without leakage delay as well as complex-valued neural networks (CVNNs) with both leakage and discrete delays...
April 26, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28531874/sparse-subspace-clustering-for-data-with-missing-entries-and-high-rank-matrix-completion
#18
Jicong Fan, Tommy W S Chow
Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion...
April 25, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28494329/hopfield-networks-as-a-model-of-prototype-based-category-learning-a-method-to-distinguish-trained-spurious-and-prototypical-attractors
#19
Chris Gorman, Anthony Robins, Alistair Knott
We present an investigation of the potential use of Hopfield networks to learn neurally plausible, distributed representations of category prototypes. Hopfield networks are dynamical models of autoassociative memory which learn to recreate a set of input states from any given starting state. These networks, however, will almost always learn states which were not presented during training, so called spurious states. Historically, spurious states have been an undesirable side-effect of training a Hopfield network and there has been much research into detecting and discarding these unwanted states...
April 25, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28499191/spatiotemporal-signal-classification-via-principal-components-of-reservoir-states
#20
Ashley Prater
Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data. Rather than training the network node connections and weights via backpropagation in traditional recurrent neural networks, reservoirs instead have fixed connections and weights among the 'hidden layer' nodes, and traditionally only the weights to the output layer of neurons are trained using linear regression. We claim that for signal classification tasks one may forgo the weight training step entirely and instead use a simple supervised clustering method based upon principal components of reservoir states...
April 24, 2017: Neural Networks: the Official Journal of the International Neural Network Society
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