Paper Info
Title
Hybrid learning algorithm for fuzzy neuro systems
Abstract
We propose a hybrid learning algorithm for fuzzy neural network (FNN) systems, which combining the back-propagation and the genetic algorithms. Without any pre-training, the algorithm achieves high accuracy performance. Here, we make a breakthrough of the restriction of membership function to be some specific shape (e.g., triangular form, trapezoid form and shape of bell). The membership functions of the FNN are constructed by a group of line segment and then are fine tuned by genetic algorithm (GA) for achieving the mapping accuracy. The proposed training algorithm can be described as: (a) Firstly, we construct and train the FNN using the back-propagation algorithm to obtain membership functions and consequent weight vector. (b) Membership functions with a group of line segment by partitioning and sampling themselves are constructed. Thus we can represent membership functions in a string form-chromosome for genetic algorithm (GA). (c) Finally, for every partition point, we use the GA to search the optimal value and obtain the optimal membership functions. Simulation results show that the mapping capability of the FNN trained by the proposed method is much better. In addition, the application on the fuzzy rules reduction is presented to show the effectiveness of the approach.
Year
DOI
Venue
2004
10.1109/FUZZY.2004.1375482
FUZZ-IEEE
Keywords
Field
DocType
fuzzy set theory,line segment,bell shape,fuzzy neuro systems,training algorithm,backpropagation algorithm,fuzzy rules reduction,triangular shape,hybrid learning algorithm,backpropagation,membership function,fuzzy systems,fuzzy neural network system,genetic algorithms,trapezoid shape,string form chromosome,fuzzy control,fuzzy neural nets,back propagation,genetic algorithm,fuzzy neural network,sampling methods,shape
Line segment,Computer science,Fuzzy logic,Algorithm,Fuzzy set,Artificial intelligence,Fuzzy control system,Backpropagation,Artificial neural network,Membership function,Genetic algorithm,Machine learning
Conference
Volume
ISSN
ISBN
2
1098-7584
0-7803-8353-2
Citations 
PageRank 
References 
7
0.51
9
Authors
2
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Yu-Ching Lin238928.19