Computer Science ›› 2015, Vol. 42 ›› Issue (Z6): 118-121.

Previous Articles     Next Articles

Simulation and Dynamic Identification System Based on Improved Neural Network Model Algorithm

ZUO Jun and ZHOU Ling   

  • Online:2018-11-14 Published:2018-11-14

Abstract: For identifier,the connection weight of neural network corresponds to model parameter.By adjusting weight of neural network,the network outputs are approximated to the system outputs.When taking neural networks as identifier NNI and doing some training on it,network weights will become the estimation of system parameters.The traditional algorithm is improved by introducing weighted factor so as to control the impact of estimated value made by the input factors.The estimated values of parameters are always uniformly asymptotic convergence in a wide range. The network steady state were thought as the minimum point of objective function for any optimization problem.The convergence process from the initial state to the steady state is the optimization calculation.At last,simulation experiments were developed to test some specific cases and the obtained results are ideal and reasonable.

Key words: Neural network,System identification,System parameters,Identification model

[1] Bors G,Gabbouj M.Minimal topology for a radial basis functions neural networks for pattern classification[J].Digital processing,2012,4:173-188
[2] 刘金琨,沈晓蓉,赵龙.系统辨识理论及MATLAB仿真[M].北京:电子工业出版,2013:105-107
[3] Hoffbeck J P,Landgrebe D A.Covariance matrix estimation and classification with limited training data[J].IEEE Trans.Pattern Analysis and Machine Intelligence,2013,8:763-767
[4] Lawrence S,Giles C L,Tsoi A C,et al.Face recognition:a convolutional neural-network approach[J].IEEE Trans.Neural Networks,Special Issue on Neural Networks and Pattern Recognition,2011,8:114-132
[5] Virginia E-D.Biometric identification system using a radial basis network[C]∥Pro.34th Annual IEEE In[J]t.Carnahan Conf.on Security Technology,2011:47-51
[6] LiS Z,Lu J.Face recognition using the nearest feature linemethod[J].IEEE Trans.Neural Networks,2012,10:439-443
[7] Brennan V,Principe J.Face classification using a multiresolution principal component analysis[C]∥Proc.IEEE Workshop Neural Network for Signal Processing.2011:506-515
[8] Chen S,Cowan C F N,Grant P M.Orthogonal least squares algorithm for radial basis function network[J].IEEE Trans.Neural Networks,2011,2:302-310
[9] Wu S Q,Er M J.Dynamic Fuzzy Neural Networks:A Novel Approach to Function Approximation[J].IEEE Trans.Syst,Man,Cybern.Part B.2012,30:358-364
[10] Esposito A,Marinaro M,Oricchoi D,et al.Approximation ofcontinuous and discontinuous mappings by a growing neural RBF-based algorithm[J].Neural Networks,2013,25:651-665
[11] Bors A G,Pitas I.Median radial basis function neural network[J].IEEE Trans.Neural Networks,2012,23:1351-1364
[12] Myood J,Darken C J.Fast Leaning in network of locally -tuned processing units[J].Neural Computation,2011,1:281-294
[13] Girosi F,Poggio T.Networks and the best approximation property[J].Biological Cybernetics,2012,63:169-176
[14] 杨文光.权值直接确定的三角型模糊前向神经网络[J].中山大学学报:自然科学版,2013,2(2):33-37
[15] 任爱红.模糊随机过程函数列均方差一致Henstock积分的可积性[J].中山大学学报:自然科学版,2012,51(4):41-44
[16] Haykin S.Neural networks,a comprehensive foundation[M].New York:Macmillan,2012:358-366

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!