Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks

Abstract

Developing a precise dynamic model is a critical step in the design and analysis of the overhead crane system. To achieve this objective, we present a novel radial basis function neural network (RBF-NN) modeling method. One challenge for the RBF-NN modeling method is how to determine the RBF-NN parameters reasonably. Although gradient method is widely used to optimize the parameters, it may converge slowly and may not achieve the optimal purpose. Therefore, we propose the cuckoo search algorithm with membrane communication mechanism (mCS) to optimize RBF-NN parameters. In mCS, the membrane communication mechanism is employed to maintain the population diversity and a chaotic local search strategy is adopted to improve the search accuracy. The performance of mCS is confirmed with some benchmark functions. And the analyses on the effect of the communication set size are carried out. Then the mCS is applied to optimize the RBF-NN models for modeling the overhead crane system. The experimental results demonstrate the efficiency and effectiveness of mCS through comparing with that of the standard cuckoo search algorithm (CS) and the gradient method.