Extended Gauss Model and Its Simulated Annealing Algorithm

The method for global optimization by random artificial neural networks (AN2) is studied. The Gauss model is modified to an extended Gauss model, which can be used to obtain the global optimum. With the simulated annealing algorithm being taken into consideration, the simulated algorithm for the extended Gauss model is proposed. Simulation shows that the extended Gauss model has the best comprehensive behavior, the extended Gauss model running with the simulated algorithm gives the best global performance with much longer operation time than other two, and the extendedHopfield model is the worst among them.