A new type of fuzzy neural network based on a truth space approach for automatic acquisition of fuzzy rules with linguistic hedges
✍ Scribed by Shin-ichi Horikawa; Takeshi Furuhashi; Yoshiki Uchikawa
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 791 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0888-613X
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✦ Synopsis
Fuzzy reasoning methods are generally classified into two approaches: the direct approach and the truth space approach. Set,eral researches on the relationships between these approaches haL'e been reported. There has been, howeLrer, no research which discusses their utility. The authors haL~e preL, iously proposed four types of fuzzy neural networks (FNNs) called Type I, II, III, and IV. The FNNs can identify the fuzzy rules and tune the membership functions of fuzzy reasoning automatically, utilizing the learning capability of neural networks. Types III and IV, which are based on the truth space approach, can acquire linguistic fuzzy rules with the fuzzy cariables in the consequences labeled according to their linguistic truth t'alues (LTVs). HoweL~er, the expressions aL,ailable for the linguistic labeling are limited, since the L TVs are singletons. This paper presents a new type of FNN based on the truth space approach for automatic acquisition of the fuzzy rules with linguistic hedges. The new FNN, called Type V, has the L TVs defined by fuzzy sets for fuzzy rules and can express the identified fuzzy rules linguistically using the fuzzy t~ariables in the consequences with finguistic