Fuzzy Neural Network with a Fuzzy Learning Rule Emphasizing Data Near Decision Boundary

A fuzzy LVQ (Learning Vector Quantization), which is based on the fuzzification of LVQ, is proposed. This new fuzzy LVQ puts more emphasis on the input vector near decision boundary than on the input vector far from decision boundary. It prevents the outlier from deteriorating the decision boundary. The proposed fuzzy LVQ is used in the neural network, which has the control structure similar to that of ART(Adaptive Resonance Theory)-1 neural network. The result shows that Supervised IAFC (Integrated Adaptive Fuzzy Clustering) Neural Network 6 yielded fewer misclassifications than LVQ algorithm and backpropagation neural network.

[1]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[2]  Z. Zenn Bien,et al.  Supervised IAFC Neural Network Based on the Fuzzification of Learning Vector Quantization , 2006, KES.

[3]  Yong-Soo Kim,et al.  Fuzzy Neural Network Model Using a Fuzzy Learning Vector Quantization with the Relative Distance , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).

[4]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

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

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

[7]  Torsten Kuhlen,et al.  Comparative analysis of fuzzy ART and ART-2A network clustering performance , 1998, IEEE Trans. Neural Networks.

[8]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[9]  Yong Soo Kim.  Fuzzy Neural Network: with an Emphasis on Fuzzy Learning Rules , 2008 .

[10]  Korris Fu-Lai Chung,et al.  Fuzzy competitive learning , 1994, Neural Networks.

[11]  James C. Bezdek,et al.  An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering , 1997, IEEE Trans. Fuzzy Syst..

[12]  Z. Zenn Bien,et al.  INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES , 2005 .

[13]  Sunanda Mitra,et al.  An adaptive integrated fuzzy clustering model for pattern recognition , 1994, CVPR 1994.

[14]  Sunanda Mitra,et al.  Noise tolerance of adaptive resonance theory neural network for binary pattern recognition , 1991, Optics & Photonics.

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

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

[17]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[18]  Yong Soo Kim,et al.  An unsupervised neural network using a fuzzy learning rule , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[19]  Terrance L. Huntsberger,et al.  PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN RECOGNITION , 1990 .

[20]  Fu-Lai Chung,et al.  A fuzzy learning model for membership function estimation and pattern classification , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[21]  Sunanda Mitra,et al.  Integrated adaptive fuzzy clustering (IAFC) algorithm , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.