The POP learning algorithms: reducing work in identifying fuzzy rules

A novel fuzzy neural network, the Pseudo Outer-Product based Fuzzy Neural Network (POPFNN), and its two fuzzy-rule-identification algorithms are proposed in this paper. They are the Pseudo Outer-Product (POP) learning and the Lazy Pseudo Outer-Product (LazyPOP) leaning algorithms. These two learning algorithms are used in POPFNN to identify relevant fuzzy rules. In contrast with other rule-learning algorithms, the proposed algorithms have many advantages, such as being fast, reliable, efficient, and easy to understand. POP learning is a simple one-pass learning algorithm. It essentially performs rule-selection. Hence, it suffers from the shortcoming of having to consider all the possible rules. The second algorithm, the LazyPOP learning algorithm, truly identifies the fuzzy rules which are relevant and does not use a rule-selection method whereby irrelevant fuzzy rules are eliminated from an initial rule set. In addition, it is able to adjust the structure of the fuzzy neural network. The proposed LazyPOP learning algorithm is able to delete invalid feature inputs according to the fuzzy rules that have been identified. Extensive experimental results and discussions are presented for a detailed analysis of the proposed algorithms.

[1]  Minho Lee,et al.  A new neuro-fuzzy identification model of nonlinear dynamic systems , 1994, Int. J. Approx. Reason..

[2]  Michel Pasquier,et al.  POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Paul J. Werbos,et al.  Neurocontrol and fuzzy logic: Connections and designs , 1992, Int. J. Approx. Reason..

[4]  James M. Keller,et al.  Neural network implementation of fuzzy logic , 1992 .

[5]  Hisao Ishibuchi,et al.  Interpolation of fuzzy if-then rules by neural networks , 1994, Int. J. Approx. Reason..

[6]  Ronald R. Yager,et al.  Implementing fuzzy logic controllers using a neural network framework , 1992 .

[7]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[8]  Hiok Chai Quek,et al.  A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture , 2001, Pattern Recognit. Lett..

[9]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[10]  Isao Hayashi,et al.  Construction of fuzzy inference rules by NDF and NDFL , 1992, Int. J. Approx. Reason..

[11]  Bart Kosko,et al.  Unsupervised learning in noise , 1990, International 1989 Joint Conference on Neural Networks.

[12]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[13]  Michel Pasquier,et al.  Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles , 2001, Neural Networks.

[14]  P. J. Werbos,et al.  Backpropagation: past and future , 1988, IEEE 1988 International Conference on Neural Networks.

[15]  Ruowei Zhou,et al.  POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Rudolf Kruse,et al.  A neuro-fuzzy method to learn fuzzy classification rules from data , 1997, Fuzzy Sets Syst..

[17]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[18]  Kai Keng Ang.  POPFNN-CRI(S) : a fuzzy neural network based on the compositional rule of inference , 1998 .

[19]  Chin-Teng Lin,et al.  A neural fuzzy control system with structure and parameter learning , 1995 .

[20]  Saman K. Halgamuge,et al.  Neural networks in designing fuzzy systems for real world applications , 1994 .

[21]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[22]  Ronald R. Yager,et al.  Modeling and formulating fuzzy knowledge bases using neural networks , 1994, Neural Networks.

[23]  Hiok Chai Quek,et al.  Pseudo-outer product based fuzzy neural network fingerprint verification system , 2001, Neural Networks.

[24]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[25]  Ramon López de Mántaras,et al.  Approximate Reasoning Models , 1990 .

[26]  Ruowei Zhou,et al.  POPFNN: A Pseudo Outer-product Based Fuzzy Neural Network , 1996, Neural Networks.

[27]  C. von Altrock,et al.  A neuro-fuzzy adaptive control strategy for refuse incineration plants , 1994 .