A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

[1]  Jin-Hua She,et al.  Integrated Hybrid-PSO and Fuzzy-NN Decoupling Control for Temperature of Reheating Furnace , 2009, IEEE Transactions on Industrial Electronics.

[2]  H. M. Emara,et al.  Continuous swarm optimization technique with stability analysis , 2004, Proceedings of the 2004 American Control Conference.

[3]  Jing J. Liang,et al.  Novel composition test functions for numerical global optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[4]  Bijan Shirinzadeh,et al.  Robust Neural Network Motion Tracking Control of Piezoelectric Actuation Systems for Micro/Nanomanipulation , 2009, IEEE Transactions on Neural Networks.

[5]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Voratas Kachitvichyanukul,et al.  Particle Swarm Optimization algorithm with multiple social learning structures , 2009 .

[7]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[8]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[9]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Nadia Nedjah,et al.  Swarm Intelligent Systems , 2006, Studies in Computational Intelligence.

[11]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[12]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[13]  Qingshan Liu,et al.  Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions , 2011, IEEE Transactions on Neural Networks.

[14]  Stephen A. Billings,et al.  Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification , 2009, IEEE Transactions on Neural Networks.

[15]  Chilukuri K. Mohan,et al.  Analysis of a simple particle swarm optimization system , 1998 .

[16]  Jun Wang,et al.  Analysis and Design of a $k$ -Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function , 2010, IEEE Transactions on Neural Networks.

[17]  Xin Chen,et al.  A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[19]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[20]  Dipti Srinivasan,et al.  A Dual layered PSO Algorithm for evolving an Artificial Neural Network controller , 2007, 2007 IEEE Congress on Evolutionary Computation.

[21]  E. Ozcan,et al.  Particle swarm optimization: surfing the waves , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Hak-Keung Lam,et al.  Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Martin T. Hagan,et al.  Neural network design , 1995 .

[24]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[25]  Hamid Khaloozadeh,et al.  Neural Networks Hammerstein Model Identification Based On Particle Swarm Optimization , 2008, 2008 IEEE International Conference on Networking, Sensing and Control.

[26]  Huaguang Zhang,et al.  Global Asymptotic Stability of Reaction–Diffusion Cohen–Grossberg Neural Networks With Continuously Distributed Delays , 2010, IEEE Transactions on Neural Networks.

[27]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[28]  E. Yaz.  Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[29]  Zengqiang Chen,et al.  New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process , 2007, IEEE Transactions on Neural Networks.

[30]  Mojtaba Ahmadieh Khanesar,et al.  Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods , 2009, Appl. Soft Comput..

[31]  F. Grimaccia,et al.  PSO as an effective learning algorithm for neural network applications , 2004, Proceedings. ICCEA 2004. 2004 3rd International Conference on Computational Electromagnetics and Its Applications, 2004..