Width residual neuro fuzzy system based on random feature selection

Abstract

To shorten the operating time of the high-dimensional problems on fuzzy systems, we proposed the width residual neuro fuzzy system (WRNFS) before, but the discussion on the structure of WRNFS was insufficient, especially on the divide-and-conquer strategies of the input dimensions. In previous research, the optimization methods for WRNFS were not discussed. In this paper we proposed the first optimization method for WRNFS, which is an improved scheme for grouping the input dimensions of WRNFS, using random feature selection(RFS) to find a better solution, so as to improve the overall capability of the system. We call the width residual neuro fuzzy system based on random feature-selection as RFS-WRNFS. In this paper, the exhaustive experiment analysis and practical test of WRNFS and RFS-WRNFS are carried out on the reconstructed MG dataset, and the following conclusions are obtained: ding172 The performance of WRNFS is generally consistent when the structure of the WRNFS sub-systems and the input-output pairs are fixed; ding173 When searching for the optimal solution on the WRNFS, the time cost of exhaustive search is acceptable when the system remains in a small scale; ding174 In most cases, RFS-WRNFS carries out several random tests and produces better results than WRNFS. Furthermore, assuming that the input dimension is N and the times of attempts used to random feature selection for a better solution of WRNFS is M, we found:1) when M = 1 N, there is a certain probability to get an acceptable solution, and the system takes the shortest time; 2) When M = 2 N, there is a great chance to get an acceptable solution in a limited time; 3) When M = 3 N, best solution can be obtained with the longest search time. We suggest M = 2 N for the RFS-WRNFS for the comprehensive performance. Comparing the experiment results of exhaustive search and random feature selection, WRNFS always reaches the optimal solution by exhaustive search through a finite set in a limited time, while RFS-WRNFS in most time keeps a good balance between prediction precision and time efficiency.