Ship Synchronous Generator Modeling Based on RST and RBF Neural Networks

Abstract

AbstractShip synchronous generator modeling is the basis of control, analysis and design in the ship power systems. According to the strong non-linear relation characteristics of ship synchronous generator, a dynamic modeling method based on rough set theory (RST) and radial basis function (RBF) neural networks is presented in this paper. With the advantage of finding useful and minimal hidden patterns in data, RST is first applied to intelligent data analysis in this algorithm, including incompatible data elimination, important input nodes selection and radial basis function centers initialization, followed by a second stage adjusting the network parameters and training the weights of hidden nodes. The experimental results proved that this method could achieve greater accuracy and generalization ability.KeywordsRadial Basis FunctionRadial Basis Function Neural NetworkRadial Basis Function NetworkSynchronous GeneratorIntelligent Data AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.