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In target threat assessment of air combat, the evaluation system model is usually nonlinear and the assessment which is difficult to obtain also has some uncertain information. In order to effectively solve these problems, the Single-hidden-layer Fuzzy Recurrent Wavelet Neural Network Optimized by Genetic Algorithm (GA-SLFRWNN) is presented in this paper. In this new method, the influence factors for assessment and the ambiguity of their information are first analyzed. The RWNN are embed in the back part of FNN (fuzzy neural network) for the purpose of enhancing self-learning ability. Then GA is used to optimize the initial parameters of the model and the optimal learning rate based on Lyapunov theory is proposed. The simulation results show that the proposed algorithm improves the stability of the evaluation system, accelerates the convergence speed and enhances the prediction accuracy compared with the FNN and SLFRWNN.

Assessment of Aerial Target Threat Based on Genetic Algorithm Optimizing Fuzzy Recurrent Wavelet Neural Network