Neural network control of nonlinear dynamic systems using hybrid algorithm

A hybrid method is proposed to control a nonlinear dynamic system.This hybrid algorithm combines gradient method and Kohonen algorithm to obtain faster convergence.The proposed algorithm can considerably reduce networking time. In this paper, a hybrid method is proposed to control a nonlinear dynamic system using feedforward neural network. This learning procedure uses different learning algorithm separately. The weights connecting the input and hidden layers are firstly adjusted by a self organized learning procedure, whereas the weights between hidden and output layers are trained by supervised learning algorithm, such as a gradient descent method. A comparison with backpropagation (BP) shows that the new algorithm can considerably reduce network training time.

[1]  Dianhui Wang,et al.  Global Convergence of Online BP Training With Dynamic Learning Rate , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Mohamed Chtourou,et al.  A fuzzy neighborhood-based training algorithm for feedforward neural networks , 2007, Neural Computing and Applications.

[3]  Laxmidhar Behera,et al.  On Adaptive Learning Rate That Guarantees Convergence in Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[4]  Wei Wu,et al.  Convergence of Cyclic and Almost-Cyclic Learning With Momentum for Feedforward Neural Networks , 2011, IEEE Transactions on Neural Networks.

[5]  Mohamed Chtourou,et al.  A self-organizing map-based initialization for hybrid training of feedforward neural networks , 2011, Appl. Soft Comput..

[6]  Wei Wu,et al.  Convergence of gradient method with momentum for two-Layer feedforward neural networks , 2006, IEEE Transactions on Neural Networks.

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  Byung Ro Moon,et al.  A Hybrid Neurogenetic Approach for Stock Forecasting , 2007, IEEE Transactions on Neural Networks.

[9]  Tianyou Chai,et al.  A Nonlinear Control Method Based on ANFIS and Multiple Models for a Class of SISO Nonlinear Systems and Its Application , 2011, IEEE Transactions on Neural Networks.

[10]  Jun Wang,et al.  A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control , 2011, IEEE Transactions on Neural Networks.

[11]  Rong-Kwei Li,et al.  A new ART-counterpropagation neural network for solving a forecasting problem , 2005, Expert Syst. Appl..

[12]  Kumpati S. Narendra,et al.  Issues in the application of neural networks for tracking based on inverse control , 1999, IEEE Trans. Autom. Control..

[13]  I. Kanellakopoulos,et al.  Systematic Design of Adaptive Controllers for Feedback Linearizable Systems , 1991, 1991 American Control Conference.

[14]  Shingo Mabu,et al.  Enhancing the generalization ability of neural networks through controlling the hidden layers , 2009, Appl. Soft Comput..

[15]  Mohammad Mehdi Ebadzadeh,et al.  Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm , 2011, Applied Intelligence.

[16]  Yi Guo,et al.  Nonlinear decentralized control of large-scale power systems , 2000, Autom..

[17]  Chih-Min Lin,et al.  Neural-network-based robust adaptive control for a class of nonlinear systems , 2011, Neural Computing and Applications.

[18]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[19]  George W. Irwin,et al.  A hybrid linear/nonlinear training algorithm for feedforward neural networks , 1998, IEEE Trans. Neural Networks.

[20]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[21]  Insoo Lee,et al.  Neural network indirect adaptive control with fast learning algorithm , 1996, Neurocomputing.

[22]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[23]  Shuzhi Sam Ge,et al.  Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities , 2010, IEEE Transactions on Neural Networks.

[24]  P.J. Werbos,et al.  An overview of neural networks for control , 1991, IEEE Control Systems.

[25]  Hongbo Zhou,et al.  Neural network-based sliding mode adaptive control for robot manipulators , 2011, Neurocomputing.

[26]  Mohamed Chtourou,et al.  A Hybrid Training Algorithm for Feedforward Neural Networks , 2006, Neural Processing Letters.

[27]  Shaocheng Tong,et al.  Adaptive Fuzzy Robust Output Feedback Control of Nonlinear Systems With Unknown Dead Zones Based on a Small-Gain Approach , 2014, IEEE Transactions on Fuzzy Systems.

[28]  S. C. Tong,et al.  Adaptive Neural Network Decentralized Backstepping Output-Feedback Control for Nonlinear Large-Scale Systems With Time Delays , 2011, IEEE Transactions on Neural Networks.

[29]  Nikola K. Kasabov,et al.  HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.

[30]  Hao Yu,et al.  Improved Computation for Levenberg–Marquardt Training , 2010, IEEE Transactions on Neural Networks.

[31]  Hongxing Li,et al.  Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks , 2004, IEEE Trans. Neural Networks.

[32]  Grigorios N. Beligiannis,et al.  Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique , 2005, IEEE Transactions on Instrumentation and Measurement.

[33]  Siavash Khosravi,et al.  Design of a fast convergent backpropagation algorithm based on optimal control theory , 2012 .

[34]  Kumpati S. Narendra,et al.  Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework , 2004, IEEE Transactions on Neural Networks.

[35]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[36]  Nikolai S. Rubanov.  The layer-wise method and the backpropagation hybrid approach to learning a feedforward neural network , 2000, IEEE Trans. Neural Networks Learn. Syst..

[37]  Léon Personnaz,et al.  Nonlinear internal model control using neural networks: application to processes with delay and design issues , 2000, IEEE Trans. Neural Networks Learn. Syst..

[38]  Masayoshi Tomizuka,et al.  Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms , 2001, Autom..

[39]  Grigorios N. Beligiannis,et al.  Combining evolutionary and stochastic gradient techniques for system identification , 2009 .

[40]  Xuemei Ren,et al.  Neural Networks-Based Adaptive Control for Nonlinear Time-Varying Delays Systems With Unknown Control Direction , 2011, IEEE Transactions on Neural Networks.

[41]  Wassim M. Haddad,et al.  Robust adaptive control for nonlinear uncertain systems , 2003, Autom..

[42]  Nasser Sadati,et al.  Adaptive multi-model sliding mode control of robotic manipulators using soft computing , 2008, Neurocomputing.

[43]  P.V. Kokotovic,et al.  The joy of feedback: nonlinear and adaptive , 1992, IEEE Control Systems.

[44]  Ming Liu,et al.  Decentralized control of robot manipulators: nonlinear and adaptive approaches , 1999, IEEE Trans. Autom. Control..

[45]  Miguel Pinzolas,et al.  Neighborhood based Levenberg-Marquardt algorithm for neural network training , 2002, IEEE Trans. Neural Networks.

[46]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[47]  Hakan Elmali,et al.  Robust output tracking control of nonlinear MIMO systems via sliding mode technique , 1992, Autom..

[48]  Yahya H. Zweiri,et al.  A three-term backpropagation algorithm , 2003, Neurocomputing.

[49]  Stefano Fanelli,et al.  A new class of quasi-Newtonian methods for optimal learning in MLP-networks , 2003, IEEE Trans. Neural Networks.

[50]  Wlodzislaw Duch,et al.  Variable step search algorithm for feedforward networks , 2008, Neurocomputing.

[51]  George W. Irwin,et al.  Improving neural network training solutions using regularisation , 2001, Neurocomputing.

[52]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[53]  Derrick H. Nguyen,et al.  Neural networks for self-learning control systems , 1990 .

[54]  Alexander G. Loukianov,et al.  Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks , 2007, IEEE Transactions on Neural Networks.