关键词: 平淡粒子滤波, 自组织模糊神经网络, 误差下降率, 模糊规则, 隶属函数

Abstract: In this paper, a Self-Organizing Fuzzy Neural Network (SOFNN) based on Unscented Particle Filter (UPF) was designed and developed. The UPF was used to estimate the parameters of the SOFNN and better result was gotten. The generating criterion of fuzzy rules based on the pruning strategy of the error reduction ratio was introduced. The width of membership function was established as the state and the ideal output as the measurement. The UPF was used to learn parameters. The two typical simulations, nonlinear function approximation and system identification, were done to validate the UPF-SOFNN. It can be seen from the results of simulation that the UPF-SOFNN has a more compact structure and better generalization than the other algorithms.

Key words: Unscented Particle Filter (UPF), Self-Organizing Fuzzy Neural Network (SOFNN), Error Reduction Ratio (ERR), fuzzy rule, membership function