Analysis of fuzzy cognitive maps with multi-step learning algorithms in valuation of owner-occupied homes

In the paper some analysis of multi-step learning algorithms for fuzzy cognitive map (FCM) is given. FCMs, multi-step supervised learning based on gradient method and unsupervised one based on nonlinear Hebbian learning (NHL) algorithm are described. Comparative analysis of these methods to one-step algorithms, from the point of view of the speed of convergence of learning algorithm and the influence on the decision support system for the valuation of owner-occupied homes was performed. Simulation results were obtained with the use of ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The results show that the implementation of the multi-step technique gives certain possibilities to get quicker values of target FCM relations and improve the operation of the learned system.