Artificial neural network weights optimization design based on MEC algorithm

Mind evolutionary computation (MEC) is a new approach of evolutionary computation. In this paper, it is adopted to train the weights of artificial neural network (ANN) to solve premature convergence problem of BP algorithm and genetic algorithm. The coding method of taking individual weights as the center of normal distribution is proposed, and information of network weights is used. Dynamic searching method is used, and weights are trained successfully. The simulation result shows that the new method is better than the common BP algorithm and genetic algorithm.