Research and design of distributed neural networks with chip training algorithm

To solve the bottleneck of memory in current prediction of protein secondary structure program, a chip training algorithm for a Distributed Neural Networks based on multi-agents is proposed in this paper. This algorithm evolves the global optimum by competition from a group of neural network agents by processing different groups of sample chips. The experimental results demonstrate that this method can effectively improve the convergent speed, has good expansibility, and can be applied to the prediction of protein secondary structure of middle and large size of amino-acid sequence.