Variable Length IPO and its application in concurrent design and train of ANFIS systems

摘要

In this paper, a new version of IPO called (VLIPO- Variable Length Inclined Planes System Optimization Algorithm) has been provided primarily. Then, an efficient tool for simultaneous design and training of an ANFIS (adaptive neuro-fuzzy inference system) has been proposed using the mentioned algorithm. It should be noted that till the present time, related research has been only dealing with finding type and location of membership functions or proposing a method of training such networks. Length of standard versions of heuristic algorithms has been mainly the reason for not specifying the type and location of membership and training an ANFIS network simultaneously. For the same reason, first, a new version of the IPO is introduced with such factors being variable. Then, such capability is used for specifying the type and location of membership functions and simultaneous training of an ANFIS classifier. It goes without saying that the idea of making variations in search factors could be also implemented in other heuristic methods. Some of them have been reported in the research. Therefore, the presented idea in the paper may be applied to other heuristic methods and used in the design and simultaneous training of an ANFIS. So, the results from the comparison made between implementing the proposed method and other methods of which a version with a variable length has been previously reported (PSO, ACOR, DE, and GA) have been presented in several well-known databases. The results showed the better execution of ANFIS classifier designed by VLIPO compared to other heuristic methods.