Fuzzy inference neural network

Abstract A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer; (2) it has an ability to select a suitable number of rules adaptively; and (3) it can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.

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

[2]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[3]  C. S. George Lee,et al.  Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems , 1994, IEEE Trans. Fuzzy Syst..

[4]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[5]  Bart Kosko,et al.  Hybrid fuzzy ellipsoidal learning , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[6]  Yoshiyuki Shimokawa.  Structure and Rules Autoextraction of Fuzzy Reasoning Neural Network , 1993 .

[7]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[8]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[9]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[10]  Chuen-Tsai Sun,et al.  Rule-base structure identification in an adaptive-network-based fuzzy inference system , 1994, IEEE Trans. Fuzzy Syst..

[11]  Masafumi Hagiwara,et al.  Kohonen feature maps as a supervised learning machine , 1993, IEEE International Conference on Neural Networks.

[12]  Derek A. Linkens,et al.  A fuzzified CMAC self-learning controller , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[13]  Masao Nakagawa,et al.  Fuzzy inference neural networks which automatically partition a pattern space and extract fuzzy if-then rules , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[14]  Hisao Ishibuchi,et al.  Neural networks that learn from fuzzy if-then rules , 1993, IEEE Trans. Fuzzy Syst..

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

[16]  Takeshi Yamakawa.  INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS , 1991 .

[17]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[18]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[19]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[20]  Li-Xin Wang.  Training of fuzzy logic systems using nearest neighborhood clustering , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[21]  Derek A. Linkens,et al.  Learning control using fuzzified self-organizing radial basis function network , 1993, IEEE Trans. Fuzzy Syst..

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

[23]  Bart Kosko,et al.  Fuzzy function learning with covariance ellipsoids , 1993, IEEE International Conference on Neural Networks.

[24]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..