CHAOTIC NEURAL FUZZY ASSOCIATIVE MEMORY

Abstract

A chaotic neuron model with the linear saturating activation function is analyzed. The model accounts for the property of relative refractoriness, that is, gradual recovery of responsiveness of a biological neuron after a stimulus is applied to the neuron. A neural network model composed of chaotic neurons with the linear saturating activation functions, which includes the generalized Brain-State-in-a-Box (gBSB) model as a special case, is proposed and analyzed. The proposed model is then used to implement associative memory. The existence and stability of equilibrium points of the model are analyzed. Fuzzy logic is used to tune associative memory parameters for the purpose of directing the network trajectory to visit memory patterns with sought features. Simulation results are presented to illustrate the effectiveness of the memory retrieval capability.