Hybrid BFO-PSO and Kernel FCM for the Recognition of Pilot Performance Influenced by Simulator Movement Using Diffusion Maps

Abstract

This paper proposed a novel data reduction and classification method to analyze high-dimensional and complicated flight data. This method integrated diffusion maps and kernel fuzzy c-means algorithm (KFCM) to recognize two types of simulator modes at different tasks. To optimize the unknown parameters of the KFCM, a hybrid bacterial foraging oriented (BFO) and particle swarm optimization (PSO) algorithm was also presented in this paper. This algorithm increased the possibility of finding the optimal values within a short computational time and avoided to be trapped in the local minima. By using the proposed approach, this paper obtained meaningful clusters respecting the intrinsic geometry of the standard data set, and illustrated the phenomenon that the pilots vestibular influenced pilot performance and control system under the Manual departure task.