Modeling and identification of irrigation station using fuzzy c-mean clustering algorithms based on particle swarm optimization

The fuzzy clustering model makes up one of the best approaches to show an excellent ability to identify and model nonlinear systems. In literature, several algorithms were used to solve this kind of case. Fuzzy c-means (FCM) is one of the most used clustering methods because it is efficient, straightforward, and easy to implement. However, the FCM has its weakness for example, the algorithm is generally sensitive to initialization and is easily trapped in local optima due to its non-convex objective function. To avoid these problems, heuristic methods introduced by many researchers such as particle swarm optimization (PSO). So, it is a robust strategy for optimization problem. In this paper, a fuzzy clustering method based on FCM and PSO is study which make use of the merits of both algorithms to find a new model of the irrigation station. Experimental results applied to station of irrigation show that the hybrid algorithm (FCM-PSO) is efficient and can reveal encouraging results. Our analysis indicates that the PSO improves the performance of the fuzzy c-means (FCM) algorithm.