Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

A novel adaptive hybrid rule network (AHRN) based on Takagi-Sugeno (TS) fuzzy rules is proposed to resolve chaotic system prediction problems. This model automatically adjusts its structure and dynamically establishes rule sets (apart from statically) to adapt in learning new samples. For the learning process, the opinion leader-based quantum-behaved particle swarm optimization (OLB-QPSO) algorithm is proposed. This algorithm uses composed particles generated according to AHRN and emphasizes the importance of the composed particle with the highest fitness based on a social communication law. To improve the chance of finding the best global solution, the movement of the composed particle is affected by the subparticles as inner factors and by the swarm as outer factor.Three chaotic time series experiments are performed to validate the proposed method. Results show that AHRN that uses the OLB-QPSO with composed particles can effectively provide the appropriate rules to search for solutions in a wide space and significantly improve the probability of obtaining the optimal global solution. A novel adaptive model is proposed to deal with the chaotic time series prediction.Dynamic rule selection mechanism is proposed for the model to establish the rule set.Opinion leader-based QPSO (OLB-QPSO) algorithm is proposed to learn this model.The composed particle with a multiple structure is used in OLB-QPSO.

[1]  Charlie C. L. Wang,et al.  From laser-scanned data to feature human model: a system based on fuzzy logic concept , 2003, Comput. Aided Des..

[2]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[3]  Madasu Hanmandlu,et al.  Structure identification of generalized adaptive neuro-fuzzy inference systems , 2003, IEEE Trans. Fuzzy Syst..

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[6]  Chang-Hwan Im,et al.  Multimodal function optimization based on particle swarm optimization , 2006, IEEE Transactions on Magnetics.

[7]  Russell C. Eberhart,et al.  Evolutionary computation implementations , 2007 .

[8]  Jerry M. Mendel,et al.  A Non-Singleton Interval Type-2 Fuzzy Logic System for universal image noise removal using Quantum-behaved Particle Swarm Optimization , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[9]  Yupu Yang,et al.  Incremental smooth support vector regression for Takagi-Sugeno fuzzy modeling , 2014, Neurocomputing.

[10]  Panos Liatsis,et al.  A GMDH-based fuzzy modeling approach for constructing TS model , 2012, Fuzzy Sets Syst..

[11]  Gang Xiao,et al.  Product interface reengineering using fuzzy clustering , 2008, Comput. Aided Des..

[12]  Russell C. Eberhart,et al.  chapter seven – The Particle Swarm , 2001 .

[13]  Witold Pedrycz,et al.  Hybrid Intelligent Systems - Analysis and Design , 2007, Studies in Fuzziness and Soft Computing.

[14]  Meng Joo Er,et al.  NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches , 2005, Fuzzy Sets Syst..

[15]  David K. Wright,et al.  From on-line sketching to 2D and 3D geometry: a system based on fuzzy knowledge , 2000, Comput. Aided Des..

[16]  Yupu Yang,et al.  Differential evolution and quantum-inquired differential evolution for evolving Takagi-Sugeno fuzzy models , 2011, Expert Syst. Appl..

[17]  Liang Wang,et al.  Complex systems modeling via fuzzy logic , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Leandro dos Santos Coelho,et al.  A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers , 2012 .

[19]  Li Weihua,et al.  Optimization Algorithm Based on T-S Fuzzy Model of Self-Adaptive Disturbed Particle Swarm Optimization and Neural Network , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[20]  Andreas Bastian.  Identifying fuzzy models utilizing genetic programming , 2000, Fuzzy Sets Syst..

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[23]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[24]  Liu Dong,et al.  Elite Particle Swarm Optimization with mutation , 2008, 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing.

[25]  Babak Nadjar Araabi,et al.  A new systematic design for Habitually Linear Evolving TS Fuzzy Model , 2012, Expert Syst. Appl..

[26]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  Sung-Kwun Oh,et al.  Identification of fuzzy models using a successive tuning method with a variant identification ratio , 2008, Fuzzy Sets Syst..

[29]  J. Buckley.  Sugeno type controllers are universal controllers , 1993 .

[30]  Wenbo Xu,et al.  An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position , 2008, Appl. Math. Comput..

[31]  Thomas Stützle,et al.  Ant Algorithms , 2000, Lecture Notes in Computer Science.

[32]  Songfeng Lu,et al.  Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization , 2010, Expert Syst. Appl..

[33]  T. Martin McGinnity,et al.  Predicting a Chaotic Time Series using Fuzzy Neural network , 1998, Inf. Sci..

[34]  Wenbo Xu,et al.  A Novel and More Efficient Search Strategy of Quantum-Behaved Particle Swarm Optimization , 2007, ICANNGA.

[35]  Korris Fu-Lai Chung,et al.  Multilevel fuzzy relational systems: structure and identification , 2002, Soft Comput..

[36]  Na Wang,et al.  A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model , 2009, Expert Syst. Appl..

[37]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[38]  Jun Li,et al.  An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory Exerting to Local Optimal Position ⋆ , 2013 .

[39]  David K. Wright,et al.  Freehand drawing system using a fuzzy logic concept , 1999, Comput. Aided Des..

[40]  Anula Khare,et al.  A review of particle swarm optimization and its applications in Solar Photovoltaic system , 2013, Appl. Soft Comput..

[41]  Ding Gang,et al.  Convolution Sum Discrete Process Neural Network and Its Application in Aeroengine Exhausted Gas Temperature Prediction , 2012 .

[42]  Shaohua Xu,et al.  A Modified Quantum-Behaved Particle Swarm Optimization for Constrained Optimization , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[43]  Fang Liu,et al.  chaotic quantum-behaved particle swarm optimization based on lateral nhibition for image matching , 2012 .