Based On Support Vector Machine Modeling Of Ship Turbines

The modeling of the Synchronous generator is an important method to study the characteristic of the generator. and plays a fundamental role in simulation,optimization and control. Modeling by mechanism usually describes the process by some mathematical equations based on analyzing process principles and making some assumptions and simplifications. As a typical kind of empirical modeling method Artificial Neural Networks has been applied to many problems for its good performance in solving nonlinear problems.But ANN has some disadvantages such as overfitting,local minimum,etc.because its theory is based on Empirical Risk Minimization(ERM)principle.Support Vector Machines(SVM) is a new learning method based on Statistical Learning Theory(SLT). SVM based on Structural Risk Minimization (ARM) principle overcomes ANN inherent disadvantangs and greatly improves models'generalization ability. In this thesis we discuss the application of SVM in modeling shipping generator processes in detail.Support Vector Machines Regression (SVMR) is an important area of SLT, and it possesses good characteristic on modeling of time-sequence events. Because of its advantage on modeling of nonlinear system,SVMR has become a new researchful field in recent years. Supporting by mathematics theory. SVMR nonlinear modeling and SVMR nonlinear control theory not only has a simple model, but also provides a new control theory which is suitable for complex nonlinear system.Firstly, The theoretical basis and algorithms of Statistical Learning Theory and Support Vector Machines is studied. And then the modeling method based on SVM is researched. The paper also describes the principle and method systematically for SVMR modeling.The result of application was practised in modeling shipping generator. Support Vector Machines method turned out to be a practical approach after being researched in comparision with conventional Artificial Neural Networks...