Convergence of Cohen-Grossberg neural networks with delays and time-varying coefficients.
Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach
This paper investigates the stability in an exponential sense of complex-valued Bidirectional Associative Memory (BAM) neural networks with time delays under the stochastic and impulsive effects. By utilizing the contracting mapping theorem, the existence and uniqueness of the equilibrium point for the proposed complex-valued neural networks are verified. Moreover, based on the Lyapunov - Krasovskii functional construction, matrix inequality techniques and stability theory, some novel time-delayed...
Delay-dependent asymptotic stability for neural networks with time-varying delays.
Delay-dependent asymptotic stability of Cohen-Grossberg models with multiple time-varying delays.
Delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays.
Delay-range-dependent global robust passivity analysis of discrete-time uncertain recurrent neural networks with interval time-varying delay.
Design of a multivariable neural controller for control of a nonlinear MIMO plant
The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship's current forward...
Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform
This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.
Dissipativity of neural networks with continuously distributed delays.
Doubly periodic traveling waves in a cellular neural network with linear reaction.
Drive network to a desired orbit by pinning control
The primary objective of the present paper is to develop an approach for analyzing pinning synchronization stability in a complex delayed dynamical network with directed coupling. Some simple yet generic criteria for pinning such coupled network are derived analytically. Compared with some existing works, the primary contribution is that the synchronization manifold could be chosen as a weighted average of all the nodes states in the network for the sake of practical control tactics, which displays...
Dynamic analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with delays.
Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment
Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose...
Dynamic properties of cellular neural networks.
Dynamical analysis of DTNN with impulsive effect.
Dynamics of Cohen-Grossberg neural networks with mixed delays and impulses.
Edge of chaos in reaction diffusion CNN model
In this paper, we study the dynamics of a reaction-diffusion Cellular Nonlinear Network (RD-CNN) nodel in which the reaction term is represented by Brusselator cell. We investigate the RD-CNN dynamics by means of describing function method. Comparison with classical results for Brusselator equation is provided. Then we introduce a new RD-CNN model with memristor coupling, for which the edge of chaos regime in the parameter space is determined. Numerical simulations are presented for obtaining dynamic...
Emotion learning: Solving a shortest path problem in an arbitrary deterministic environment in linear time with an emotional agent
The paper presents an algorithm which solves the shortest path problem in an arbitrary deterministic environment with n states with an emotional agent in linear time. The algorithm originates from an algorithm which in exponential time solves the same problem, and the agent architecture used for solving the problem is an NN-CAA architecture (neural network crossbar adaptive array). By implementing emotion learning, the linear time algorithm is obtained and the agent architecture is modified. The...
Emotional learning based intelligent speed and position control applied to neurofuzzy model of switched reluctance motor
Ensemble neural network approach for accurate load forecasting in a power system
The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component...