TY - JOUR
T1 - Intelligent decision-making algorithm based on bounded FART-Q
AU - Zhou, Yanan
AU - Gong, Guanghong
N1 - Publisher Copyright:
©, 2015, Beijing University of Aeronautics and Astronautics (BUAA). All right reserved.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Adaptive resonance theory
KW - Artificial neural network
KW - Fuzzy set theory
KW - Intelligent decision-making
KW - Q learning
UR - https://www.scopus.com/pages/publications/84924731154
U2 - 10.13700/j.bh.1001-5965.2014.0076
DO - 10.13700/j.bh.1001-5965.2014.0076
M3 - 文章
AN - SCOPUS:84924731154
SN - 1001-5965
VL - 41
SP - 96
EP - 101
JO - Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
JF - Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
IS - 1
ER -