Title :
Cellular automata learning of fuzzy cognitive map
Author :
Chunmei, Lin ; Yue, He
Author_Institution :
Dept. of Compute, Shaoxing Univ., Shaoxing, China
fDate :
June 30 2012-July 2 2012
Abstract :
A learning methodology is proposed for automatically constructing fuzzy cognitive map. In the proposed method, the evolutionary mechanism of cellular automata is used to learn the connection matrix of FCM. One-dimension cellular automata are used to code weight parameters, the cellular states are chosen within the range [0, 1] to form a cell space. In order to guide the optimization direction effectively and accelerate the speed of convergence, a mutation operator is added in the algorithm. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.
Keywords :
cellular automata; convergence; evolutionary computation; fuzzy neural nets; learning (artificial intelligence); 1D cellular automata; cellular automata learning; cellular state; connection matrix; convergence speed; evolutionary mechanism; fuzzy cognitive map; learning methodology; mutation operator; neural network; one-dimension cellular automata; optimization direction; weight parameter; Automata; Computational modeling; Convergence; Fuzzy cognitive maps; Learning automata; Vectors; Fuzzy Cognitive Map; cellular automat; learning; system modeling;
Conference_Titel :
System Science and Engineering (ICSSE), 2012 International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-1-4673-0944-8
Electronic_ISBN :
978-1-4673-0943-1
DOI :
10.1109/ICSSE.2012.6257202
