Identification of finite state automata with a class of recurrent neural networks.
A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in the encoding, identification, and extraction of finite state automata (FSAs). Simulation results show that the identification of FSAs using the proposed network, trained by the hybrid greedy simulated annealing with a modified cost function in the training stage, generally exhibits better performance than the conventional identification procedures.
Won
Sung Hwan
SH
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. sunghwanwon@hotmail.com
Song
Iickho
I
Lee
Sun Young
SY
Park
Cheol Hoon
CH
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation / Q000379:methods*; D003198:Computer Simulation; D055641:Mathematical Concepts; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011340:Problem Solving / Q000502:physiology