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

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https://www.readbyqxmd.com/read/27923168/improved-exponential-convergence-result-for-generalized-neural-networks-including-interval-time-varying-delayed-signals
#1
G Rajchakit, R Saravanakumar, Choon Ki Ahn, Hamid Reza Karimi
This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs...
November 9, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27887770/robust-learning-in-spikeprop
#2
Sumit Bam Shrestha, Qing Song
Training a Spiking Neural Network using SpikeProp and its derivatives faces stability issues. Surges, marked by a sudden rise in learning cost, are a common occurrence during the learning process. They disrupt the learning process and often destabilize the process resulting in failure. A proper learning rate, which is neither too small nor too big, is important to minimize surges. Furthermore, external disturbances due to imperfection in sample data as well as internal disturbances are additional destabilizing source during the learning process...
November 8, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27914262/a-new-switching-control-for-finite-time-synchronization-of-memristor-based-recurrent-neural-networks
#3
Jie Gao, Peiyong Zhu, Ahmed Alsaedi, Fuad E Alsaadi, Tasawar Hayat
In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued maps, sufficient conditions to ensure FTS of MNNs are obtained under the two cases of 0<α<1 and α=0, and it is derived that α=0 is the critical value of 0<α<1. Next, it is discussed deeply on the relation between the parameter α and the synchronization time...
November 4, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27890606/a-new-hyperbox-selection-rule-and-a-pruning-strategy-for-the-enhanced-fuzzy-min-max-neural-network
#4
Mohammed Falah Mohammed, Chee Peng Lim
In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data...
November 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27889240/a-balanced-motor-primitive-framework-can-simultaneously-explain-motor-learning-in-unimanual-and-bimanual-movements
#5
Ken Takiyama, Yutaka Sakai
Certain theoretical frameworks have successfully explained motor learning in either unimanual or bimanual movements. However, no single theoretical framework can comprehensively explain motor learning in both types of movement because the relationship between these two types of movement remains unclear. Although our recent model of a balanced motor primitive framework attempted to simultaneously explain motor learning in unimanual and bimanual movements, this model focused only on a limited subset of bimanual movements and therefore did not elucidate the relationships between unimanual movements and various bimanual movements...
November 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27855307/a-limit-cycle-self-organizing-map-architecture-for-stable-arm-control
#6
Di-Wei Huang, Rodolphe J Gentili, Garrett E Katz, James A Reggia
Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks...
October 29, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27842241/finite-time-synchronization-of-uncertain-coupled-switched-neural-networks-under-asynchronous-switching
#7
Yuanyuan Wu, Jinde Cao, Qingbo Li, Ahmed Alsaedi, Fuad E Alsaadi
This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results...
October 29, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27846430/synchronization-of-discrete-time-neural-networks-with-delays-and-markov-jump-topologies-based-on-tracker-information
#8
Xinsong Yang, Zhiguo Feng, Jianwen Feng, Jinde Cao
In this paper, synchronization in an array of discrete-time neural networks (DTNNs) with time-varying delays coupled by Markov jump topologies is considered. It is assumed that the switching information can be collected by a tracker with a certain probability and transmitted from the tracker to controller precisely. Then the controller selects suitable control gains based on the received switching information to synchronize the network. This new control scheme makes full use of received information and overcomes the shortcomings of mode-dependent and mode-independent control schemes...
October 27, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27842242/stabilization-of-metastable-dynamical-rotating-waves-in-a-ring-of-unidirectionally-coupled-sigmoidal-neurons-due-to-shortcuts
#9
Yo Horikawa
Effects of shortcut connection on metastable dynamical rotating waves in a ring of sigmoidal neurons with unidirectional excitatory coupling are considered. A kinematical equation describing the propagation of wave fronts is derived with a sign function for the output function of neurons. Unstable rotating waves can be stabilized in the presence of an inhibitory shortcut. When a shortcut is excitatory and connects the most distant neurons, the dynamical metastability of rotating waves is lost. The duration of transient rotating waves then increases only linearly with the number of neurons, not exponentially...
October 25, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814465/an-online-incremental-orthogonal-component-analysis-method-for-dimensionality-reduction
#10
Tao Zhu, Ye Xu, Furao Shen, Jinxi Zhao
In this paper, we introduce a fast linear dimensionality reduction method named incremental orthogonal component analysis (IOCA). IOCA is designed to automatically extract desired orthogonal components (OCs) in an online environment. The OCs and the low-dimensional representations of original data are obtained with only one pass through the entire dataset. Without solving matrix eigenproblem or matrix inversion problem, IOCA learns incrementally from continuous data stream with low computational cost. By proposing an adaptive threshold policy, IOCA is able to automatically determine the dimension of feature subspace...
October 14, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814463/mittag-leffler-stability-of-fractional-order-neural-networks-in-the-presence-of-generalized-piecewise-constant-arguments
#11
Ailong Wu, Ling Liu, Tingwen Huang, Zhigang Zeng
Neurodynamic system is an emerging research field. To understand the essential motivational representations of neural activity, neurodynamics is an important question in cognitive system research. This paper is to investigate Mittag-Leffler stability of a class of fractional-order neural networks in the presence of generalized piecewise constant arguments. To identify neural types of computational principles in mathematical and computational analysis, the existence and uniqueness of the solution of neurodynamic system is the first prerequisite...
October 14, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814464/complete-stability-of-delayed-recurrent-neural-networks-with-gaussian-activation-functions
#12
Peng Liu, Zhigang Zeng, Jun Wang
This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3(k) equilibrium points with 0≤k≤n, among which 2(k) and 3(k)-2(k) equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i...
October 8, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814467/attribute-based-decision-graphs-a-framework-for-multiclass-data-classification
#13
João Roberto Bertini, Maria do Carmo Nicoletti, Liang Zhao
Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances...
October 5, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814462/echo-state-networks-for-data-driven-downhole-pressure-estimation-in-gas-lift-oil-wells
#14
Eric A Antonelo, Eduardo Camponogara, Bjarne Foss
Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements...
October 4, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814461/recovering-low-rank-and-sparse-matrix-based-on-the-truncated-nuclear-norm
#15
Feilong Cao, Jiaying Chen, Hailiang Ye, Jianwei Zhao, Zhenghua Zhou
Recovering the low-rank, sparse components of a given matrix is a challenging problem that arises in many real applications. Existing traditional approaches aimed at solving this problem are usually recast as a general approximation problem of a low-rank matrix. These approaches are based on the nuclear norm of the matrix, and thus in practice the rank may not be well approximated. This paper presents a new approach to solve this problem that is based on a new norm of a matrix, called the truncated nuclear norm (TNN)...
October 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814460/pinning-controlled-synchronization-of-delayed-neural-networks-with-distributed-delay-coupling-via-impulsive-control
#16
Wangli He, Feng Qian, Jinde Cao
This paper investigates pinning synchronization of coupled neural networks with both current-state coupling and distributed-delay coupling via impulsive control. A novel impulse pinning strategy involving pinning ratio is proposed and a general criterion is derived to ensure an array of neural networks with two different topologies synchronizes with the desired trajectory. In order to handle the difficulties of high-dimension criteria, some inequality techniques and matrix decomposition methods through simultaneous diagonalization of two matrices are introduced and low-dimensional criteria are obtained...
October 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814466/evaluation-of-extreme-learning-machine-for-classification-of-individual-and-combined-finger-movements-using-electromyography-on-amputees-and-non-amputees
#17
Khairul Anam, Adel Al-Jumaily
The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features...
October 1, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27890605/developmental-metaplasticity-in-neural-circuit-codes-of-firing-and-structure
#18
Yoram Baram
Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage...
September 30, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814468/a-modular-architecture-for-transparent-computation-in-recurrent-neural-networks
#19
Giovanni S Carmantini, Peter Beim Graben, Mathieu Desroches, Serafim Rodrigues
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata...
September 24, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27718389/delay-distribution-dependent-h%C3%A2-state-estimation-for-delayed-neural-networks-with-x-v-dependent-noises-and-fading-channels
#20
Li Sheng, Zidong Wang, Engang Tian, Fuad E Alsaadi
This paper deals with the H∞ state estimation problem for a class of discrete-time neural networks with stochastic delays subject to state- and disturbance-dependent noises (also called (x,v)-dependent noises) and fading channels. The time-varying stochastic delay takes values on certain intervals with known probability distributions. The system measurement is transmitted through fading channels described by the Rice fading model. The aim of the addressed problem is to design a state estimator such that the estimation performance is guaranteed in the mean-square sense against admissible stochastic time-delays, stochastic noises as well as stochastic fading signals...
September 19, 2016: Neural Networks: the Official Journal of the International Neural Network Society
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