Journal of Decision Engineering

Todays, analysis of chaotic systems is one of the crucial challenges of researchers in machine learning field. The ability of classification and extracting implicit knowledge of such kind of data enables us to provide powerful prediction systems in various fields of engineering and economics.

So far, various methods such as evolutionary algorithms, neural networks and etc. have been employed to process this type of data.However, an ideal and universal solution to that has not been reached. In these circumstances, addressing new algorithms that can help in this direction seems necessary. For this purpose, we present a new computational algorithm based brain emotional learning in this paper. This method used the reinforcement learning to govern dynamic data and find the rules and extract knowledge in chaotic systems. To do so, we apply our algorithm for classification of brain signals (one of chaotic systems).At the end, we prove the efficiency by comparing the results of the proposed algorithm with two other famous one.