On some necessary and sufficient conditions for a recurrent neural network model with time delays to generate oscillations
In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network ...
A fast algorithm for robust mixtures in the presence of measurement errors
In experimental and observational sciences, detecting atypical, peculiar data from large sets of measurements has the potential of highlighting candidates of interesting new types of objects that deserve more detailed domain-specific followup study. ...
Blind multiuser detector for chaos-based CDMA using support vector machine
The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the ...
On the selection of weight decay parameter for faulty networks
The weight-decay technique is an effective approach to handle overfitting and weight fault. For fault-free networks, without an appropriate value of decay parameter, the trained network is either overfitted or underfitted. However, many existing results ...
Control of unknown nonlinear systems with efficient transient performance using concurrent exploitation and exploration
Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma ...
Backpropagation and ordered derivatives in the time scales calculus
Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using ...
Approximate robust policy iteration using multilayer perceptron neural networks for discounted infinite-horizon Markov decision processes with uncertain correlated transition matrices
We study finite-state, finite-action, discounted infinite-horizon Markov decision processes with uncertain correlated transition matrices in deterministic policy spaces. Existing robust dynamic programming methods cannot be extended to solving this ...
Automatic induction of projection pursuit indices
Projection techniques are frequently used as the principal means for the implementation of feature extration and dimensionality reduction for machme learing applications. A well established and broad class of such projection techniques is the projection ...
Fast support vector data descriptions for novelty detection
Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-...
Robust exponential stability of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays
This paper is concerned with the problem of exponential stability for a class of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays and known or unknown parameters. The jumping parameters are determined by a ...
An extension of the standard mixture model for image segmentation
Standard Gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the ...
Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A ...
Marginalized neural network mixtures for large-scale regression
For regression tasks, traditional neural networks (NNs) have been superseded by Gaussian processes, which provide probabilistic predictions (input-dependent error bars), improved accuracy, and virtually no overfitting. Due to their high computational ...
Neural-network-based adaptive leader-following control for multiagent systems with uncertainties
A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted ...
Exponential H∞synchronization of general discrete-time chaotic neural networks with or without time delays
This brief studies exponential H∞ synchronization of a class of general discrete-time chaotic neural metworks with external disturbance. On the basis of the drive-response concept and H∞ control theory, and using Lyapunov-Krasovskii (or Lyapunov) ...
Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay
In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the ...
Multistability of recurrent neural networks with time-varying delays and the piecewise linear activation function
In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (...
