Adaptive Neural-Fuzzy Inference And Its Application On The Ship Rudder Damping

Adaptive neural-fuzzy inference (ANFIS) and its application on the nonlinear ship rudder damping system are studied in this thesis. The ANFIS design method is a blend intelligent system which combines the Fuzzy Logic System(FLS) and the Annual Neural Network (ANN) and uses their's strongpoints. It expresses the FLS by the connection structure of ANN , meanwhile realize the parameter optimization of the FLS rules by the self-learning function of ANN.The biggest point of ANFIS is that it is a modeling method based on data. Through a great deal of datas not experience or instinct can get the Fuzzy membership and rules of ANFIS, so ANFIS especially fits the complex industry control processes without specialist knowledge. This design mainly finish through the below work:First the non-linear ship model is programmed in M file and uncoiled by Runge-Kutta. The wave disturbance function is programmed to be used to ship model. In order to get the training data of ANFIS,PID control is adopted to the ship model which prepare for the ANFIS training, meanwhile get the simulation result. The roll reduction can reach about 30%.Second the ANFIS toolbox is used in matlab, training the preliminary Fuzzy Logic System. Before the training a sugeno Fuzzy system is designed as the preliminary FLS which use the training data to train. It can be seen that the Fuzzy membership have changed. The FLS is saved after training and used to the ship model. The roll reduction is about 25%.In order to understand the ANFIS further , a PID control based on BP network is designed. The scale coefficient of PID is adjust by the self-learning of network, resolve the abuse which it need test repeatly to get the three PID parameter. It can be proved there is good effect.