Attractors of Discrete Cellular Neural Networks
نویسندگان
چکیده
The dynamic behavior of discrete cellular neural networks (DCNN) with zero threshold value, which is strict, is mainly studied. For the DCNN with zero threshold value and no self-feedback, if a state is a fixed point, then a lot of unstable points are given, and under some conditions these unstable points can converge to the fixed point. In this paper, the properties of k-attractor of the DCNN are mainly studied, and some conditions are obtained under which the kattractor is a fixed point, and lots of unstable points are attracted to the fixed point. The results obtained here on k-attractor improve the results in the previous references.
منابع مشابه
Linear matrix inequality approach for synchronization of chaotic fuzzy cellular neural networks with discrete and unbounded distributed delays based on sampled-data control
In this paper, linear matrix inequality (LMI) approach for synchronization of chaotic fuzzy cellular neural networks (FCNNs) with discrete and unbounded distributed delays based on sampled-data controlis investigated. Lyapunov-Krasovskii functional combining with the input delay approach as well as the free-weighting matrix approach are employed to derive several sufficient criteria in terms of...
متن کاملFINITE-TIME PASSIVITY OF DISCRETE-TIME T-S FUZZY NEURAL NETWORKS WITH TIME-VARYING DELAYS
This paper focuses on the problem of finite-time boundedness and finite-time passivity of discrete-time T-S fuzzy neural networks with time-varying delays. A suitable Lyapunov--Krasovskii functional(LKF) is established to derive sufficient condition for finite-time passivity of discrete-time T-S fuzzy neural networks. The dynamical system is transformed into a T-S fuzzy model with uncertain par...
متن کاملThe number of direct attractors in discrete state neural networks
The maximunl possible nunlber of isotropic direct attractors of
متن کاملCounting and classifying attractors in high dimensional dynamical systems.
Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors ...
متن کاملAN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS
In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...
متن کامل