Application and Research of the Granular Evolutionary Neural Network Algorithms for the Complex Network

Abstract

Research on an "Granular Evolutionary Neural Network Algorithms (GENNA)" is applied to the complex network. The theory of the granular quotient space is introduced to the neural network. At first input variables of the neural network are granulated to equivalence classes, so that the input variables of the network structure can be simplified, and have certain clustering characteristics and strong diversity, and then the network parameters and the weights are optimized using evolutionary algorithms, so as to avoid neural network to fall into the local extremum.The experimental results show that the algorithm effectively narrow the search space and accelerate the speed of convergence , and It is feasibility and effectiveness.