With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are ex...

By analyzing the dynamics behavior and parameter distribution of the transiently chaotic neural network, we propose an improved transiently neural network model with new embedded back-end chaotic dynamics for combinatorial optimization problem and test it on the maximum clique problem. With the new embedded back-end chaotic dynamics, the proposed model can get enough chaotic dynamics to do glob...

To model mammalian olfactory neural systems, a chaotic neural network entitled K-set has been constructed. This neural network with nonconvergent “chaotic” dynamics simulates biological pattern recognition. This paper reports the characteristics of the KIII set and applies it to text classification. Compared with conventional pattern recognition algorithms, its accuracy and efficiency are demon...

Recently, chaotic neural networks have been paid much attention to, and contribute toward solving TSP. We study the existence of chaos in a discrete-time neural network. Chaotic behavior is an inside essence of stochastic processes in nonlinear deterministic system. The investigation provides a theoretical confirmation on the scenario of transient chaos for the system. All the parameter conditi...

In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of ...