Sonar array azimuth control system based on genetic neural network

The control strategy of genetic neural network (GNN) combines the good performance of back-propagation (BP) in weight learning and genetic algorithm (GA) in gaining global optimum. Firstly, the control strategy optimizes initial samples of the control system by GA, and then, weights and thresholds of the neuron are trained by applying a GNN approach, so that the performance parameter of the controllerpsilas neural network is optimized and the global searching ability of the system is improved as well. This paper proposes computation steps of the control strategy based on GNN, and applies this strategy to the control system of the sonar array azimuth. In order to test the performance of the system, this system also selected suitable parameter and carried on simulation. The simulation results show that applying the control strategy based on GNN rather than BP itself, the controller can reach a higher precision.