Application and analysis of BSB model with delay

In this paper we discuss the convergence property of a family of Brain-state-in-a-Box (BSB) models with delay. We propose a convergence theorem of the BSB with delay. We have performed a detailed convergence analysis of this network and found convergence theorem under proper assumptions of the weight matrices of this network: ones is symmetric and the other is row diagonal dominant. Meanwhile, theoretical analysis demonstrates that the BSB with delay performs much better than the original one in updating to an equilibrium point basedon Hamming distance. In practical application, the delay items are considered as noise-items, which has many advantage. The advantage of the method is the ability to transmit equilibrium points to satisfactory Solution of application, which keep the evolution by the process of neuron selection from random variation.