A neural network architecture for classification of fuzzy inputs

Abstract A neural network for classification problems with fuzzy inputs is proposed. A fuzzy input is represented as an LR-type fuzzy set. A generalized pocket algorithm, called fuzzy pocket algorithm, that uses LR-type fuzzy sets operations and defuzzification method is proposed to train a linear threshold unit (LTU). This LTU node will classify as many fuzzy input instances as possible. Afterward, FV nodes that represent fuzzy vectors will then be generated and expanded, by proposed FVGE learning algorithm, to classify those fuzzy input instances that cannot be classified by the LTU node. The similarity degree between FV nodes and fuzzy inputs is measured by the fuzzy subsethood degree. The network structure is automatically generated. The number of hidden nodes generated depends on the overlapping degree of training instances. Besides, on-line learning is supplied, and parameters used are few and insensitive. The relationship between proposed model and hyperbox-based classifiers, e.g., Fuzzy ART series and Fuzzy Min-Max series, is also discussed. Two sample problems, heart disease and knowledge-based evaluator, are considered to illustrate the working of the proposed model. The experimental results are very encouraging.

[1]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[2]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[3]  Chin-Teng Lin,et al.  Real-time supervised structure/parameter learning for fuzzy neural network , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[4]  Hideo Tanaka,et al.  An architecture of neural networks for input vectors of fuzzy numbers , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[5]  Madan M. Gupta,et al.  On fuzzy neuron models , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

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

[7]  James M. Keller,et al.  Evidence aggregation networks for fuzzy logic inference , 1992, IEEE Trans. Neural Networks.

[8]  Kaoru Hirota,et al.  Fuzzy logic neural networks: design and computations , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

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

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

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[13]  D. Dubois,et al.  Fuzzy real algebra: Some results , 1979 .

[14]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[15]  Hamid R. Berenji,et al.  A reinforcement learning--based architecture for fuzzy logic control , 1992, Int. J. Approx. Reason..

[16]  Naoki Hara,et al.  Fuzzy rule extraction from a multilayered neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[17]  Patrick K. Simpson.  Fuzzy min-max classification with neural networks , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[18]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[19]  Hahn-Ming Lee,et al.  Supervised fuzzy ART: training of a neural network for pattern classification via combining supervised and unsupervised learning , 1993, IEEE International Conference on Neural Networks.

[20]  Takeshi Yamakawa,et al.  A design algorithm of membership functions for a fuzzy neuron using example-based learning , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[21]  Sankar K. Pal,et al.  Multilayer perceptron, fuzzy sets, and classification , 1992, IEEE Trans. Neural Networks.

[22]  Shouhong Wang,et al.  Fuzzy set representation of neural network classification boundaries , 1991, IEEE Trans. Syst. Man Cybern..

[23]  James M. Keller,et al.  Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks , 1992, Int. J. Approx. Reason..

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

[25]  H. C. Card,et al.  Linguistic interpretation of self-organizing maps , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[26]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[27]  James M. Keller,et al.  Incorporating Fuzzy Membership Functions into the Perceptron Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks - Part 2: Clustering , 1993, IEEE Trans. Fuzzy Syst..

[29]  Paul E. Utgoff,et al.  Perceptron Trees : A Case Study in ybrid Concept epresentations , 1999 .

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

[31]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[32]  Hong Feng Yin,et al.  A Connectionist Incremental Expert System Combining Production Systems and Associative Memory , 1991, Int. J. Pattern Recognit. Artif. Intell..

[33]  Yoichi Hayashi,et al.  A neural expert system using fuzzy teaching input , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[34]  F. S. Wong,et al.  Fuzzy feature extraction using a class of neural network , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.