Abstract
A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer, (2) it has an ability to select a suitable number of rules adaptively; and (3) It can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.
| Original language | English |
|---|---|
| Pages (from-to) | 223-239 |
| Number of pages | 17 |
| Journal | Neurocomputing |
| Volume | 14 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1997 Feb 28 |
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Keywords
- Fuzzy inference
- Neural networks
- Self-organization
ASJC Scopus subject areas
- Artificial Intelligence
- Cellular and Molecular Neuroscience
Cite this
Fuzzy inference neural network. / Nishina, Takatoshi; Hagiwara, Masafumi.
In: Neurocomputing, Vol. 14, No. 3, 28.02.1997, p. 223-239.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fuzzy inference neural network
AU - Nishina, Takatoshi
AU - Hagiwara, Masafumi
PY - 1997/2/28
Y1 - 1997/2/28
N2 - A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer, (2) it has an ability to select a suitable number of rules adaptively; and (3) It can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.
AB - A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer, (2) it has an ability to select a suitable number of rules adaptively; and (3) It can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.
KW - Fuzzy inference
KW - Neural networks
KW - Self-organization
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U2 - 10.1016/S0925-2312(96)00036-7
DO - 10.1016/S0925-2312(96)00036-7
M3 - Article
AN - SCOPUS:0031588795
VL - 14
SP - 223
EP - 239
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
IS - 3
ER -