A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

被引:222
作者
Castro, Juan R. [1 ]
Castillo, Oscar [2 ]
Melin, Patricia [2 ]
Rodriguez-Diaz, Antonio [1 ]
机构
[1] UABC Univ, Tijuana, Mexico
[2] Tijuana Inst Technol, Div Grad Studies & Res, Dept Comp Sci, Tijuana 22500, Mexico
关键词
Interval type-2 fuzzy neural networks; Interval type-2 fuzzy neuron; Hybrid learning algorithm; Interval type-2 fuzzy inference systems; LOGIC SYSTEMS; IDENTIFICATION; SETS;
D O I
10.1016/j.ins.2008.10.016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule's antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANF1S is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:2175 / 2193
页数:19
相关论文
共 40 条
[1]
ABRAHAM A, 2003, STUDIES FUZZINESS SO, P314
[2]
[Anonymous], 1996, Neuro-Fuzzy and Soft Computing
[3]
[Anonymous], 1996, Neural Network Design
[4]
[Anonymous], STUDIES FUZZINESS SO, DOI DOI 10.1007/978-3-7908-1770-6_1
[5]
Building fuzzy inference systems with the interval type-2 fuzzy logic toolbox [J].
Castro, J. R. ;
Castillo, O. ;
Melin, P. ;
Martinez, L. G. ;
Escobar, S. ;
Camacho, I. .
ANALYSIS AND DESIGN OF INTELLIGENT SYSTEMS USING SOFT COMPUTING TECHNIQUES, 2007, 41 :53-+
[6]
CASTRO JR, 2005, THESIS TIJUANA I TEC
[7]
Cowder R.S., 1990, 1990 CONNECTIONIST M, P117
[8]
Design of interval type-2 fuzzy sliding-mode controller [J].
Hsiao, Ming-Ying ;
Li, Tzuu-Hseng S. ;
Lee, J. -Z. ;
Chao, C. -H. ;
Tsai, S. -H. .
INFORMATION SCIENCES, 2008, 178 (06) :1696-1716
[9]
Karnik N.N., 1998, Introduction to Type-2 Fuzzy Logic Systems
[10]
Applications of type-2 fuzzy logic systems to forecasting of time-series [J].
Karnik, NN ;
Mendel, JM .
INFORMATION SCIENCES, 1999, 120 (1-4) :89-111