Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution

The traditional fuzzy neural network often uses BP algorithm to optimize parameters when conducting parameter identification. However, BP algorithm tends to be trapped in local extremum. In view of the shortcomings of this method, this paper combines the differential evolution algorithm with the BP algorithm, and proposes an improved differential evolution BP algorithm to optimize the fuzzy neural network forecasting network traffic. In order to solve problems such as slow convergence speed and tendency of premature convergence existing in differential evolution algorithm, an improved differential evolution algorithm using the adaptive mutation operator and Gaussian disturbance crossover operator aims to improve the mutation of standard differential evolution algorithm and the design of crossover operators. To validate the effectiveness of it, this optimized fuzzy neural network forecasting algorithm is applied to four standard test functions and the actual network traffic. Simulation results show that the convergence speed and forecasting accuracy of the proposed algorithm are better than those of the traditional fuzzy neural network algorithm. It improves not only the generalization ability of the fuzzy neural network but also the forecasting accuracy of the network traffic.