Study On Chaos Control And Chaotic Optimization With Its Applications In Test Generation For Combinational Logic Circuits

Chaos control and chaotic optimization is one of important fields in scientific chaos research. In this dissertation, firstly, the theories and methods about chaos control and chaotic optimization are studied. Based above, some new approaches for chaos control and chaotic optimization are proposed. Furthermore, the application of chaotic optimization for test generation of combinational logic circuits is investigated in detail, and new methods for test generation are proposed based on chaotic optimization. Several valuable and important results are achieved.The main contribution and valuable results of this dissertation can be listed as following:1. Based on one type of one-dimensional return maps of chaotic systems, two impulsive control methods for chaos suppression, namely, constant impulsive control and feedback impulsive control method, are studied. With these methods, controlling chaos in nonlinear damping pendulum is discussed in detail. Simulation results demonstrate the effectiveness and robustness to chaos suppression. With combination of neural networks and internal model control, an internal model control scheme based on BP neural networks is proposed for controlling chaos. The simulation results based on a typical chaotic system, namely, Duffing oscillator, illustrate the effectiveness and strong robustness and adaptability.2. Combined chaotic searching with gradient algorithm of Hopfield neural networks, an optimization algorithm based on chaos is proposed. Based on the Hopfield neural network model for combinational circuit test generation, a new test generation algorithm with chaotic searching is developed. The algorithm can overcome the shortcoming of gradient algorithm and has a globally searching ability. Simulation results demonstrate that the algorithm can enhance the efficiency of test generation.3. A chaotic neural networks model for combinational circuit is derived from the Hopfield neural network model by adding a self-feedback term. Based on the chaotic neural network model, a test generation approach is proposed. A simplealgorithm and an improved algorithm to controlling the self-feedback term is given respectively. Experiment results show that both algorithms has a high efficiency for test generation, and the test generation probability of the improved algorithm can get up to 100%.4. Combined chaotic dynamics with genetic algorithm, an improved genetic algorithm —chaos genetic algorithm with a new crossover and mutation scheme controlled by a chaotic series is proposed. A test generation method based on chaos genetic algorithm is proposed, in which a chaos genetic algorithm is used for minimizing the energy function of the Hopfield neural network model for combinational circuit test generation. Simulation results show that the scheme has a globally searching ability, speeds up test generation and enhances the efficiency of test generation.5. To account for the problem of minimizing for complete fault test set, a special chaotic searching algorithm and chaos genetic algorithm is designed. Two minimizing approaches for fault test set based on chaotic searching and chaos genetic algorithm are proposed respectively. Simulation results show that both algorithms improve significantly globally searching speed and the method based on chaos genetic algorithm is especially fit for minimizing complex large scale fault test set.