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

基于GA-SLFRWNN的空中目标威胁评估

School of Automatic, Shenyang University of Aerospace, Shenyang 100136, China

Received: 13 May 2018

Abstract

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.

摘要

针对空战中目标威胁评估系统非线性、评估难度大且富含不确定信息的问题，研究了基于遗传算法优化模糊递归小波神经网络（single-hidden-layer fuzzy recurrent wavelet neural network optimized by genetic algorithm，GA-SLFRWNN）的目标威胁评估方法。首先通过分析威胁评估的影响因素及其信息的模糊性，将RWNN嵌入FNN的后件部分，以实现增强自学习能力的目的，然后采用GA对模型初始参数进行优化选取，并提出了基于李雅普诺夫理论的最优学习率。仿真实验表明：相比于FNN和FRWNN，该算法提高了系统的稳定性，加快了收敛速度，增强了预测精度。