Chaos Optimization And Its Control In Applied Research, In Large System Optimization

Chaotic optimization is one of important fields in scientific chaos research. In this dissertation, firstly, the theories and methods about chaos and chaotic network's optimization are studied. Based above, some new approaches for improving on chaos and chaotic network's optimization are proposed. Furthermore, the applications of chaotic optimization for the problems of nonlinear constraint optimization and large-scale optimization control are investigated in detail. Several valuable and important results are achieved.The main contribution and valuable results of this dissertation can be listed as following:The dynamic characteristics of transiently chaotic networks (TCNN) that they are quite sensitively depended on the value of the self-feedback connection weights and the annealing function that intensively influences the veracity and search speed of TCNN module are studied. Based on above analyses, we proposed an optimal strategy for value of the self-feedback connection weights. Simulation results demonstrate that the optimal strategy can accelerate the search speed and guarantee the assurance of the veracity of the optimal arithmetic. To deal with the deficiencies of the neural network model based on Hopfield neural network (HNN) for nonlinear constraint optimization problems that is easily trapped in local minimum, a novel optimization network model based on transiently chaotic network (TCNN) is proposed in this paper. Because TCNN has richer and more flexible dynamics compared to HNN, this network model that combined with Lagrange multiplier theory has higher ability of searching for globally optimal solutions to the nonlinear constraint optimization problems. Its asymptotic stability is proved and its equilibrium point is the optimal point of the original problem. The simulation results illustrate the effectiveness of this optimal network algorithm.Based on the analyses of the nonseparable steady state problem for large scale systems, where the overall objective function is not additive form with respect to subsystems, chaos optimization algorithm is applied into the solution of optimal values by the use of the property of ergodicity of chaos. Furthermore, chaos optimization algorithm successfully solves the nonseparable steady state problem for large scale system which exist conflictions. The simulation results illustrate this method is simple and easy to implement.Because transiently chaotic neural network (TCNN) has richer and more flexible dynamics compared to Hopfield neural network (HNN), hierarchical optimization network algorithm based on TCNN is applied into overcome the drawback of optimization control problem for large-scale systems using Hopfield network that is easily trapped in local minimum. The simulation results show this network algorithm has higher ability of searching for globally optimal solutions to the steady state optimization control problem for large-scale systems.