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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第6期13卷 403-412页

作者： Hasan ABBASI NOZARI Hamed DEHGHAN BANADAKI Mohammad MOKHTARE Somayeh HEKMATI VAHED Department of Mechatronics Faculty of EngineeringIslamic Azad UniversityScience and Research BranchTehranIran

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.

关键词： non-linear system identification Recurrent local linear neuro-fuzzy (RLLNF) network Local linear model tree(LOLIMOT) Neural network (NN) Industrial winding process

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non-linear Chemical Process Modelling and Application in Epichlorhydrine Production Plant Using Wavelet Networks

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Chinese Journal of Chemical Engineering 2002年第4期10卷 435-443页

作者：黄德先金以慧张杰 A.J.Morris Department of Automation Tsinghua University 北京 100084 CPACT Merz Court University of Newcastle Newcastle upon Tyne NE1 7RU 英国

A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients can be estimated by a linear algorithm. Thewavelet neural network holds some advantages superior to other typesof neural networks. First, its network structure is easy to specifybased on its theoretical analysis and intuition. Secondly, networktraining does not rely on stochastic gradient type techniques andavoids the problem of poor convergence or undesirable local minima.

关键词： wavelet neural network non-linear system identification hybrid neuralnetwork

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identification of non-linear Dynamic Model of UUV Based on ESN Neural Network

Identification of Non-linear Dynamic Model of UUV Based on E...

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第三十届中国控制会议

作者： BIAN Xinqian,MOU Chunhui Harbin Engineering University,Harbin 150001,P.R.China

＜正＞Unmanned underwater vehicle（UUV） is a highly complex nonlinear dynamic system,and neural network has the ability to arbitrary approximate nonlinear system in ***,echo state network（ESN） is a new type recurrent neural network based on state *** improve the accuracy of UUV’s dynamic model,this paper based on the use of echo state networks（ESN） of the system identification method,using "meta-learning" strategy for offline training ESN network and genetic algorithm to optimize the main parameters,to remove the difficulty of choosing the ESN *** method was applied to approximate of dynamic model of six degree of freedom of UUV,and build on the dynamic ***,the simulation proved that the network structure identification algorithm has a good approximation ability and fast training speed.

关键词： Unmanned underwater vehicle(UUV) non-linear system identification Recurrent neural network Echo state network Dynamic model

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