Ship Domain Identi cation Using Fast and Accurate Online Self-organizing Parsimonious Fuzzy Neural Networks
In this paper,we propose a novel ship domain model identi ed by the Fast and Accurate Online Self-organizing Parsimonious Fuzzy Neural Network (FAOS-PFNN),which is an effective and powerful algorithm for nonlinear system identi-cations.The blocking area is introduced to be the reference model of ship domains to generate testing and checking databases for online modeling based on the FAOS-PFNN.The main features of our proposed method are as follows:(1)a mass of reason-able input-output data pairs possessing the complex nonlinear dynamics of ship domains could be randomly extracted;(2)based on the dependable databases,the intelligent ship domain model could be online identi ed by the FAOS-PFNN while training data pairs sequentially arrives;(3)dynamic and static parameters of own and target ships encountered could be reasonably and comprehensively incorporated into the resulting fuzzy neural network model of ship domains;and,(4)the shape and size of ship domains could be implemented by three independent fuzzy neural systems based on the FAOS-PFFN.It is shown that the identi ed ship domain model could capture well the key nonlinear properties of ship domains over a wide range.Simulation studies demonstrate the high performance of identi cation and generalization in the proposed intelligent ship domain model.
WANG Ning TAN Yue LIU Shao-Man
Marine Engineering College,Dalian Maritime University,Dalian 116026,P.R.China Navigation College,Dalian Maritime University,Dalian 116026,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）