Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA

In this paper, a modified rule generation approach with self-constructing property for neuro-fuzzy system modelling is proposed. In structure identification stage, input-output patterns are divided into the clusters and interval type-2 membership functions are generated roughly. Interval type-2 Takagi-Sugeno-Kang (TSK) neuro-fuzzy structure is fine tuned by quantum inspired bacterial foraging algorithm (QBFA) in parameter identification stage to achieve higher precision, a recursive least squares (RLS) estimator is used to update consequent parameters. Comparisons with two type-1 neuro-fuzzy systems on three nonlinear functions and chaotic Mackey-Glass time series show that the proposed systems can approximate the target with little error. Experiments are also executed involving the proposed systems for modelling flue gas denitrification efficiency of a thermal power plant. It is verified by the results that interval type-2 neuro-fuzzy structure can learn knowledge from input-output data set with the aid of QBFA and hybrid training progresses are able to improve its performance.

[1]  Junmin Wang,et al.  Development and experimental studies of a control-oriented SCR model for a two-catalyst urea-SCR system , 2011 .

[2]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..

[3]  Oscar Castillo,et al.  An improved method for edge detection based on interval type-2 fuzzy logic , 2010, Expert Syst. Appl..

[4]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[5]  Adam Niewiadomski,et al.  On Finity, Countability, Cardinalities, and Cylindric Extensions of Type-2 Fuzzy Sets in Linguistic Summarization of Databases , 2010, IEEE Transactions on Fuzzy Systems.

[6]  Dongpu Cao,et al.  System Modeling and Pressure Control of a Clutch Actuator for Heavy-Duty Automatic Transmission Systems , 2016, IEEE Transactions on Vehicular Technology.

[7]  Oscar Castillo,et al.  Handling Uncertainty in Controllers Using Type-2 Fuzzy Logic , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[8]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[9]  Oscar Castillo,et al.  Bio-Inspired Optimization Methods , 2012 .

[10]  Guoliang Zhao,et al.  A Comparison between Quantum Inspired bacterial Foraging Algorithm and GA-like Algorithm for Global Optimization , 2012, Int. J. Comput. Intell. Appl..

[11]  Chen-Sen Ouyang,et al.  An approach for construction and learning of interval type-2 TSK neuro-fuzzy systems , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[12]  Dongrui Wu,et al.  Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers , 2006, Eng. Appl. Artif. Intell..

[13]  Oscar Montiel,et al.  Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic , 2007, Inf. Sci..

[14]  Hui Zhang,et al.  Combustion and emission characteristics of a direct-injection gasoline engine using the MMT fuel additive gasoline , 2015 .

[15]  Jerry M. Mendel,et al.  Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments , 2011, IEEE Transactions on Industrial Electronics.

[16]  Jerry M. Mendel,et al.  New results about the centroid of an interval type-2 fuzzy set, including the centroid of a fuzzy granule , 2007, Inf. Sci..

[17]  Mojtaba Ahmadieh Khanesar,et al.  Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Oscar Castillo,et al.  Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Oscar Castillo,et al.  Interval Type-2 TSK Fuzzy Logic Systems Using Hybrid Learning Algorithm , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[20]  Shie-Jue Lee,et al.  A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning , 2003, IEEE Trans. Fuzzy Syst..

[21]  Wan-Jui Lee,et al.  A TSK-type neurofuzzy network approach to system modeling problems , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Oscar Castillo,et al.  A review on the design and optimization of interval type-2 fuzzy controllers , 2012, Appl. Soft Comput..

[23]  Gerardo M. Mendez,et al.  Interval Type-1 Non-Singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP , 2007, FOCI.

[24]  Oscar Castillo,et al.  Interval type-2 fuzzy logic and modular neural networks for face recognition applications , 2009, Appl. Soft Comput..

[25]  Hui Zhang,et al.  NOx Sensor Ammonia-Cross-Sensitivity Factor Estimation in Diesel Engine Selective Catalytic Reduction Systems , 2015 .

[26]  Ruixuan Li,et al.  Type-2 fuzzy description logic , 2011, Frontiers of Computer Science in China.

[27]  Gerardo M. Mendez,et al.  Entry temperature prediction of a hot strip mill by a hybrid learning type-2 FLS , 2006, J. Intell. Fuzzy Syst..

[28]  Jerry M. Mendel,et al.  On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling , 2011, Inf. Sci..

[29]  Jerry M. Mendel,et al.  Advances in type-2 fuzzy sets and systems , 2007, Inf. Sci..

[30]  Milos Manic,et al.  Interval Type-2 fuzzy voter design for fault tolerant systems , 2011, Inf. Sci..

[31]  Türkay Dereli,et al.  Industrial applications of type-2 fuzzy sets and systems: A concise review , 2011, Comput. Ind..

[32]  Hani Hagras,et al.  A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation , 2010, IEEE Transactions on Fuzzy Systems.

[33]  Oscar Castillo,et al.  An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory , 2004, Inf. Sci..

[34]  Gerardo M. Mendez,et al.  Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods , 2009, Inf. Sci..

[35]  Ning Wang,et al.  Type-1/type-2 fuzzy logic systems optimization with RNA genetic algorithm for double inverted pendulum , 2015 .

[36]  Ricardo Martínez-Soto,et al.  Optimization of Interval Type-2 Fuzzy Logic Controllers for a Perturbed Autonomous Wheeled Mobile Robot Using Genetic Algorithms , 2009, Soft Computing for Hybrid Intelligent Systems.

[37]  Miin-Shen Yang,et al.  On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering , 2009, Comput. Math. Appl..

[38]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[39]  Oscar Castillo,et al.  A review on interval type-2 fuzzy logic applications in intelligent control , 2014, Inf. Sci..

[40]  Enrique E. Mombello,et al.  Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems , 2011, Expert Syst. Appl..

[41]  Witold Pedrycz,et al.  Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization , 2011, Inf. Sci..

[42]  Jerry M. Mendel,et al.  Uncertainty measures for interval type-2 fuzzy sets , 2007, Inf. Sci..

[43]  Chin-Teng Lin,et al.  A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications , 2014, IEEE Transactions on Industrial Electronics.

[44]  I. Burhan Türksen,et al.  Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters , 2007, IEEE Transactions on Fuzzy Systems.

[45]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[46]  G.M. Mendez.  Orthogonal-back propagation hybrid learning algorithm for type-2 fuzzy logic systems , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[47]  Jerry M. Mendel,et al.  Stability analysis of type-2 fuzzy systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[48]  Gerardo M. Mendez,et al.  Hybrid learning mechanism for interval A2-C1 type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems , 2013, Inf. Sci..

[49]  Tzuu-Hseng S. Li,et al.  Design of interval type-2 fuzzy sliding-mode controller , 2008, Inf. Sci..

[50]  Ali Selamat,et al.  Modeling permeability and PVT properties of oil and gas reservoir using hybrid model based on type-2 fuzzy logic systems , 2015, Neurocomputing.

[51]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[52]  Po-Jen Cheng,et al.  Recursive SVD-Based Least Squares Algorithm with Forgetting Factors for Neuro-fuzzy Modeling , 2013, 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.