@article{(Open Science Index):https://publications.waset.org/pdf/10003886,
	  title     = {Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules},
	  author    = {Hirofumi Miyajima and  Kazuya Kishida and  Noritaka Shigei and  Hiromi Miyajima},
	  country	= {},
	  institution	= {},
	  abstract     = {Most of self-tuning fuzzy systems, which are
automatically constructed from learning data, are based on the
steepest descent method (SDM). However, this approach often
requires a large convergence time and gets stuck into a shallow
local minimum. One of its solutions is to use fuzzy rule modules
with a small number of inputs such as DIRMs (Double-Input Rule
Modules) and SIRMs (Single-Input Rule Modules). In this paper,
we consider a (generalized) DIRMs model composed of double
and single-input rule modules. Further, in order to reduce the
redundant modules for the (generalized) DIRMs model, pruning and
generative learning algorithms for the model are suggested. In order
to show the effectiveness of them, numerical simulations for function
approximation, Box-Jenkins and obstacle avoidance problems are
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {10},
	  number    = {3},
	  year      = {2016},
	  pages     = {473 - 479},
	  ee        = {https://publications.waset.org/pdf/10003886},
	  url   	= {https://publications.waset.org/vol/111},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 111, 2016},