Abstract
Fuzzy adaptive resonance theory (ART) with bounded side length was proposed to address the problem emerged while applying fuzzy ART to intelligent decision-making. Integrating the modified fuzzy ART and Q learning algorithm, bounded fuzzy ART-Q learning (FART-Q) intelligent decision-making network was built. The original fuzzy ART might make unreasonable classifications only according to the fuzzy similarity between input vector and weight vector, without considering the physical meaning of the state variables. To solve this problem, a modified algorithm was proposed, strengthening the resonance condition of fuzzy ART with bounded side length. The improvement made it possible both to limit the side length according to the physical meaning of the state variables and to reduce the number of categories. The minefield navigation simulation was conducted to verify the availability and effectiveness of bounded FART-Q. Compared with the original fuzzy ART, the modified algorithm is able to make classifications more reasonably with higher success rate and less operation time.
| Original language | English |
|---|---|
| Pages (from-to) | 96-101 |
| Number of pages | 6 |
| Journal | Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics |
| Volume | 41 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1 Jan 2015 |
Keywords
- Adaptive resonance theory
- Artificial neural network
- Fuzzy set theory
- Intelligent decision-making
- Q learning
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