A novel optimization algorithm for BP neural network based on RS-MEA

When input dimension of BP neural network increased, the structure of BP neural network will become especially complex and it is vulnerable to fall into local optimum in the training process. Therefore, a new algorithm of BP neural network optimized by rough set (RS) and mind evolutionary algorithm (MEA) is proposed. RS theory was used as the front-end date processing system to achieve attribute reduction, which played a major role in reducing the complexity of BP neural network and shortening the operation time. In order to optimize the performance and lower convergence speed of BP neural network, mind evolutionary algorithm (MEA) is applied to compute the global optimal initial weight and threshold of BP neural network. According to simulation experiment and contrasting with traditional BP neural network, the performance of modified BP neural network is improved, and the proposed algorithm is applicable and valid.