The Study On The Wavelet Neural Network And Its Application To Ship Motion Control

Wavelet neural network combines the virtues of neural network such as the self-learning ability, adaptivity, robustness, fault-tolerance ability as well as the time-frequency local characteristics and zooming characteristics of wavelet transform, possesses virturs of global optimum approximating ability and fast processing speed. It avoids drawbacks of conventional BP neural network such as local minima and slow convergence speed, has been successfully applied in areas of system identification, pattern recognition and control. However, it is found in practical applications that there are several problems such as deficiencies in dynamic representing ability and generalization ability, which frustrates its practical applications.To improve the generalization ability of wavelet neural network, an improved residual selection learning algorithm is proposed based on Akaike information criterion. By setting optimal stop criterion for learning process, the achieved wavelet neural network enables satisfying identification accuracy as well as compact network structure, which avoids unfavorable phenomenon of over-fitting and under-fitting and guarantees the generalization ability of wavelet neural network. As a learning algorithm for wavelet neural network, residual selection algorithm evaluates the contribution of hidden neurons to the output respectively, which facilitate the adaptive adjustment of network dimension. Simulation results shows that the proposed algorithm improves the generalization capability of the wavelet neural network.To better represent the changes of system dynamics, the history information of system is introduced in the network input layer leading to the time-delay wavelet neural network. To settle the subsequent problem of too much variables in input layer, sensitivity analysis method based on relative contribution ratio is applied to decide the optimal inputs by selecting variables which have higher correlation with output, which resolves the problem of model mismatch and improve the network’s representing ability for changes of dynamical system. Aiming at the complex characteristics of ship motion such as nonlinearity, large inertia and time-varying dynamics, predictive PID controller is constructed based on the improved time-delay wavelet neural network. The wavelet neural network is performed for the online ship dynamic identification and prediction. The predictive control strategy is utilized to overcome the unfavorable effects of large inerita. Simulations of ship heading course following control were conducted and coparison study was processed with the conventional PID controller, the results shows that the proposed controller possesses higher control accuracy as well as stronger anti-interference ability.The aforesaid study results demonstrate that the improved wavelet neural network enhances the dynamic representing ability and generalization ability of wavelet neural network respectively. Its fast processing speed and nonlinear approximation ability enable that it can represent the characteristics of ship motion at sea, and can be implemented widely in field of ship motion control.