A hybrid evaluation model for flight performance based on bacterial foraging and Elman network

The evaluation of the flight performance via multiple physiological signals is an important problem in the field of flight safety. A hybrid prediction model is proposed to dispose multiple physiological signals with high dimension in this paper. Main contribution of our model is that of a novel bacterial foraging algorithm (BF) to optimize Elman neural network, which can perform parallel search and escape local minimum easily, and provide better prediction accuracy of the flight performance. Other bio-inspired algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and chaotic GA are also used to optimize the unknown parameters of the Elman network. Experimental results indicate that the proposed hybrid model based on BF algorithm and Elman network is well suited for the evaluation and prediction of the flight performance. Compared with the other public algorithms, the BF can easily identify the unknown parameters of the established models and has better optimization capability.