Showing 1-25 of 22,767 resultsfor "Index Terms":Stable Network
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Delay-Dependent Stability for Recurrent Neural Networks With Time-Varying Delays
This brief is concerned with the stability for static neural networks with time-varying delays. Delay-independent conditions are proposed to ensure the asymptotic stability of the neural network. The delay-independent conditions are less conservative than existing ones. To further reduce the conservatism, delay-dependent conditions are also derived, which can be applied to fast time-varying delays...Show More
A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks
A wireless ad hoc sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, it is essential that the network be able to self-organize. Clustering is an efficient approach to simplify the network structure and to alleviate the scalabil...Show More
Activity Invariant Sets and Exponentially Stable Attractors of Linear Threshold Discrete-Time Recurrent Neural Networks
This technical note proposes to study the activity invariant sets and exponentially stable attractors of linear threshold discrete-time recurrent neural networks. The concept of activity invariant sets deeply describes the property of an invariant set by that the activity of some neurons keeps invariant all the time. Conditions are obtained for locating activity invariant sets. Under some conditio...Show More
Delay-Dependent Approaches to Globally Exponential Stability for Recurrent Neural Networks
This brief deals with the stability analysis problem for recurrent neural networks with delay. An improved stability condition is derived to guarantee the existence of the unique equilibrium point and its globally exponential stability, which is shown with novel methods. Both delay-dependent and delay-independent stability conditions are obtained. Expressed in terms of LMIs, they can be checked us...Show More
Continuous Attractors of Lotka–Volterra Recurrent Neural Networks With Infinite Neurons
Continuous attractors of Lotka-Volterra recurrent neural networks (LV RNNs) with infinite neurons are studied in this brief. A continuous attractor is a collection of connected equilibria, and it has been recognized as a suitable model for describing the encoding of continuous stimuli in neural networks. The existence of the continuous attractors depends on many factors such as the connectivity an...Show More
Simultaneous perturbation learning rule for recurrent neural networks and its FPGA implementation
Recurrent neural networks have interesting properties and can handle dynamic information processing unlike ordinary feedforward neural networks. However, they are generally difficult to use because there is no convenient learning scheme. In this paper, a recursive learning scheme for recurrent neural networks using the simultaneous perturbation method is described. The detailed procedure of the sc...Show More
Globally Asymptotic Stability of a Class of Neutral-Type Neural Networks With Delays
Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellul...Show More
Mixed structured RBF network for direct inverse control of nonlinear systems
2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control
Year: 2009 | Conference Paper |
Cited by: Papers (3)
In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network has one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable co...Show More
IEEE Approved Draft Standard for Local and Metropolitan Area Networks: Bridges and Virtual Bridged Local Area Networks - Amendment 9: Shortest Path Bridging
IEEE P802.1aq/D4.5, DRAFT Amendment to IEEE Std 802.1Q -2011, February 2012
Year: 2012 | Standard |
This amendment specifies shortest path bridging of unicast and multicast frames, including protocols to calculate multiple active topologies that can share learnt station information, and support of a VLAN by multiple, per topology VLAN identifiers (VIDs).Show More
IEEE Approved Draft Standard for Local and Metropolitan Area Networks: Bridges and Virtual Bridged Local Area Networks - Amendment 9: Shortest Path Bridging
IEEE P802.1aq/D4.6, DRAFT Amendment to IEEE Std 802.1Q -2011, February 2012
Year: 2012 | Standard |
Cited by: Patents (218)
This amendment specifies shortest path bridging of unicast and multicast frames, including protocols to calculate multiple active topologies that can share learnt station information, and support of a VLAN by multiple, per topology VLAN identifiers (VIDs).Show More
On the stability, storage capacity, and design of nonlinear continuous neural networks
The stability, capacity, and design of a nonlinear continuous neural network are analyzed. Sufficient conditions for existence and asymptotic stability of the network's equilibria are reduced to a set of piecewise-linear inequality relations that can be solved by a feedforward binary network, or by methods such as Fourier elimination. The stability and capacity of the network is characterized by t...Show More
A new RBF network based sliding-mode control of nonlinear systems
2009 International Multiconference on Computer Science and Information Technology
Year: 2009 | Conference Paper |
Cited by: Papers (3)
In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network is a one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable c...Show More
A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks
Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is ine...Show More
Study on Stability of Equilibrium Point of Asymmetrical Internet Neural Network
2008 International Conference on Apperceiving Computing and Intelligence Analysis
Year: 2008 | Conference Paper |
The stability of equilibrium point of neural network for the large-scale dynamic system is extremely important. This article has studied the stability of equilibrium point of vector differential equation of the asymmetrical internet, proposed to utilize approximate linear equation to study the stability problem of equilibrium point of neural network. This method is simple and effective to examine ...Show More
Minimum Hop Routing Wireless Sensor Networks Based on Ensuring of Data Link Reliability
2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks
Year: 2009 | Conference Paper |
Cited by: Papers (8)
The traditional wireless sensor networks based on minimum hop routing (MHR) protocol have the disadvantage of short stable period of minimum hop gradient field (MHGF), this would lead to the data sinking unreliable easily. In this paper, in order to improve the reliability of data sinking, a minimum hop routing wireless sensor network based on link reliability guaranteed (MHR-LR) is proposed. In t...Show More
Global Robust Stability of Bidirectional Associative Memory Neural Networks With Multiple Time Delays
This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuo...Show More
Neural Network Adaptive Control for a Class of Nonlinear Uncertain Dynamical Systems With Asymptotic Stability Guarantees
In this paper, a neuroadaptive control framework for continuous- and discrete-time nonlinear uncertain dynamical systems with input-to-state stable internal dynamics is developed. The proposed framework is Lyapunov based and unlike standard neural network (NN) controllers guaranteeing ultimate boundedness, the framework guarantees partial asymptotic stability of the closed-loop system, that is, as...Show More
Enhanced energy zonal stable election protocol for clustered heterogeneous wireless sensor network
2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Wireless Sensor Network (WSN) is made up of a large number of sensor nodes with limited energy that cooperate to perform a sensing task. The major challenges in WSN mainly are node lifetime, the decrease in consumption of energy, stability of node and resultant throughput of the network and its nodes. To increase the node lifetime and to save energy, the clustering process is widely used. The effe...Show More
Stability Analysis of Discrete Hopfield Neural Networks With Delay and Its Application
2005 IEEE International Conference on Systems, Man and Cybernetics
Year: 2005 | Conference Paper |
Discrete Hopfield neural networks (DHNNs) with delay, which can deal with temporal information, are a generalization of the DHNNs without delay. This paper investigates the convergence theorems in DHNNs with delay. We present two generalized updating rules, one for serial mode and the other for parallel mode. The convergence speed of these proposed updating rules is faster than existing updating r...Show More
Convergence of generalized fuzzy bidirectional associative memory neural networks with thresholds
2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
Year: 2013 | Conference Paper |
Based on the fuzzy operator “ν” and a t-norm T, a generalized dynamical model named the fuzzy bidirectional associative memory neural networks (ν -T FBAMs) with thresholds is set up. It shows that every equilibrium of the system is Lyapunov stable if T satisfies Lipschitz condition. It is proved that the existence of the indices of the matrix U, which is the product of the system connection fuzzy ...Show More
Permitted and Forbidden Sets in Discrete-Time Linear Threshold Recurrent Neural Networks
The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks. The linear threshold transfer function has been regarded as an adequate transfer functio...Show More
On the multilayered Hopfield neural networks
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
Year: 1994 | Conference Paper |
A multilayered structure of Hopfield neural network is proposed in this paper for the purpose of reducing computational requirement during associative learning. The novel structure which may be viewed as a natural extension of a feedforward multilayered neural network from a static structure to a dynamic system consists of two visible layers and some hidden layers with only interlayer connections ...Show More
Global Exponential Stability for Uncertain Delayed Neural Networks of Neutral Type With Mixed Time Delays
Chang-Hua Lien;Ker-Wei Yu;Yen-Feng Lin;Yeong-Jay Chung;Long-Yeu Chung
The global exponential stability for a class of uncertain delayed neural networks (DNNs) of neutral type with mixed delays is investigated in this paper. Delay-dependent and delay-independent stability criteria are proposed to guarantee the robust stability and uniqueness of equilibrium point of DNNs via linear matrix inequality and Razumikhin-like approaches. Two classes of perturbations on weigh...Show More
Application of artificial neural networks in establishing regime channel relationships
2009 2nd International Conference on Computer, Control and Communication
Year: 2009 | Conference Paper |
Cited by: Papers (5)
The main purpose of this study is to evaluate the potential of simulating regime channel treatments using artificial neural networks. A collection of regime channel data with 371 data sets was collected from available literature. These data sets were randomly split into two subsets, i.e. Training and validation sets. The multi layer perceptron artificial neural network (MLP) was used to construct ...Show More
Genetic Networks and Soft Computing
The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new...Show More
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