Massively parallel fuzzy systems: the case of three spiral pattern recognition

The main objectives of this paper are: 1) to describe the working of a massively parallel fuzzy system; 2) to test the system on a new benchmark-the three spiral data set; and 3) to describe the behaviour of the system when solving the problem. The system described is aimed at solving pattern recognition problems in real-time. Pattern recognition data are subjected to non-iterative decision making through the estimation of class membership of test data. This paper describes the performance of this system on the temporal three spiral benchmark. The task is to learn three class data which lies on three distinct spirals that coil around each other and around the origin with time. There are no linear solutions to this problem. The system under consideration classifies the training set with 100% success and recognises training data with up to 98% success. The behavioural aspects of the system are also studied by quantifying the recognition rate as a function of spiral radius and rejection threshold.

[1]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[2]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[3]  Chuen-Tsai Sun,et al.  A neuro-fuzzy classifier and its applications , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[4]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[5]  Benjamin W. Wah,et al.  An automated design system for finding the minimal configuration of a feed-forward neural network , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[6]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[7]  David J. Evans,et al.  Fast learning artificial neural network (FLANN II) using the nearest neighbour recall , 1994, Neural Parallel Sci. Comput..

[8]  Dean A. Pomerleau,et al.  What's hidden in the hidden layers? , 1989 .

[9]  C. A. Harris,et al.  Fuzzy logic expert system for iron ore processing , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[10]  Sameer Singh,et al.  Classifier Systems Based on Possibility Distributions: A Comparative Study , 1997, ICANNGA.

[11]  Jiancheng Jia,et al.  Solving two-spiral problem through input data representation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Sameer Singh,et al.  Fuzzy Pattern Recognition for Knowledge-Based Systems1 , 1996 .

[13]  Lihui Chen,et al.  Solving two-spiral problem through input data encoding , 1995 .

[14]  K. Lang,et al.  Learning to tell two spirals apart , 1988 .

[15]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[16]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[17]  Sameer Singh,et al.  Fuzzy neural computing of coffee and tainted-water data from an electronic nose , 1996 .

[18]  David Levy,et al.  Book review: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence by Bart Kosko (Prentice Hall 1992) , 1992, CARN.