A Chaotic Dynamic Local Search Method for Learning Multiple-Valued Logic Networks

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of the local search method (LS), which is based on the generalized reduced gradient algorithm, hybrid local search method is proposed by incorporating chaos. Thus, LS and chaos are hybridized to form a chaotic dynamic local search method (CDLS), which reasonably combines the searching ability of LS and chaotic searching behavior. In this paper, a CDLS method based on the logistic equation is presented to learn Multiple-Valued Logic (MVL) Networks. Simulation results and comparisons with the traditional back propagation algorithm (BP) and the standard LS method show that the CDLS can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of iterations.