Competitive hopfield neural network with chaotic dynamics for partitional clustering problem

In this paper, an algorithm, named CCHN, is proposed to solve the partitional clustering problem. An outer chaotic mechanism with annealing strategy is introduced into the competitive Hopfield neural network to construct CCHN for expecting better opportunities of converging to the optimal solution. In addition to retain the competitive characteristics of the conventional competitive Hopfield neural network, CCHN displays a rich range of complex and flexible chaotic dynamics. The chaotic dynamics and the annealing strategy guarantee the powerful searching ability and the effective convergence of CCHN. Results simulated on clustering benchmark problems show that CCHN algorithm is more likely to find an optimal or near-optimal solution with a higher successful ratio than previous algorithms.