چكيده به لاتين
Fuzzy cognitive maps (FCMs) represent a graphical modeling technique based on the decision-making and reasoning rules and algorithms similar to those used by humans. The graph-like structure and the execution model of FCMs respectively allow static and dynamic analyses to be carried out. The learning algorithms of FCM that are based on expert opinion are weak in dynamic analysis, and fully automatic algorithms are weak in static analysis. In static analysis, semiautomatic algorithms are not as good as algorithms based on expert opinion; and in dynamic analysis, they are not as efficient as fully automatic algorithms. In this thesis, for providing the facility for simultaneous static and dynamic analysis, a new training algorithm called the quantum fuzzy cognitive map (QFCM) is presented. In this proposed algorithm, the quantum inspired evolutionary algorithm (QEA) and the particle swarm optimization algorithm is employed for generating static and dynamic analysis properties respectively. In the QFCM, instead of coding the presence and absence of links between concepts with 1 and 0, respectively, the probability of their existence or inexistence is modeled with a Q-bit (the smallest information unit in the QEA) and, depending on the outcome of dynamic analysis, the quantum state of this Q-bit is updated. Using a probabilistic representation instead of 0 and 1, in addition to creating diversity in the solution space, can lead to escapes from many local optima; which is an issue of concern in the optimization of FCM structure. For demonstrating the advantages and the efficacy of QFCM algorithm, it was applied on both synthetic and real-life datasets. The obtained results indicate the superiority of the proposed algorithm over the other newly-devised algorithms in this field.
