Neural Network Design Based On Evolutionary Computation Method

Artificial neural networks try to mimic the nerve system in a mammalian brain into a mathematical model. Therefore, neural networks have some desirable characteristics and capabilities similar to the brain system, such as parallel processing, learning, non-linear mapping, and generalization. Many researchers have developed neural networks as new tools in many fields such as pattern recognition, information processing, design, planning, diagnosis, and control. The past decade have witnessed a great deal of progress in both the theory and the practice of control using neural networks. After a long period of experimentation and research, neural network-based controllers are now being realized in a wide variety of fields. The practice applications are also calling for a better understanding of the theoretical principles involved. There are some problems which always make users puzzle, such as deciding the suitable types, structures and parameters of neural networks, selecting a good method for training, and analyzing the stability and robust of neural network-based control systems. In this paper, I focus my effort on how to obtain the suitable structures and parameters of neural networks.In Chapter One, I give the complete introduction of neural networks used in the field of control. Through the review of the development of control theory and the research directions of intelligent control, I point out the challenges which the control theory is facing, and I also point out the great potential of the control methods based on neural networks. After describing the structures of control systems based on neural networks carefully, I clarify some specious thoughts and summarize the problems needed to be solved urgently. In the end, I introduce the research situation of the hardware implementation of neural networks simply.In Chapter Two, I put forward a structural design method based on evolutionary algorithms. At first, I elaborate the significance of structural design, and then set out all kinds of the structural design methods which are familiar to me. Designing suitable structures for neural networks belongs to a kind of optimization questions, so it is natural to look for good optimization techniques. In recent years, evolutionary algorithms are popularly used for their population-based optimization mechanism. Genetic algorithm is well-known among them, and evolutionary programming employs the same Darwinian evolutionary principles as genetic algorithm and is implemented by the interaction of individuals in a population. These two algorithms are somewhat similar, but genetic algorithm relies on genetic operators, while evolutionary programming emphasizes the performance change from the population level. Furthermore, when evolutionary programming is used for optimization, mutation is the unique recombination operator. After taking the encoding method into consideration, I think evolutionary programming is most suitable for my research work. In general, structural evolution is accompanied by the training of connection weights. For sake of the irregularity of structures, the optimization methods based on gradient-descent can't be used to optimize the connection weights in a fixed structure, so I select genetic algorithm to finish this task. Finally, simulation results are given to illustrate the efficiency of the proposed method.