Research and Hardware Implementation of BP Neural Network
|School||Shandong University of Science and Technology|
|Course||Control Theory and Control Engineering|
|Keywords||Artificial Neural Network Hardware Realization Neuron Model Back Propagation algorithm|
Artificial neural network (the neural network, Neural Network) is a Mathematical model to imitate the thinking way of human brain. Neural network is a network structure established on the base of the modern biology research the human brain’s physiological structure. It is used to simulate the network structure and behavior of the human brain. It simplifies and abstracts the human brain by the function and structure. It is an important way of simulating human intelligence.80s of last century, the research of neural network made a breakthrough. The junction of neural network and control theory can solve the problem of complex nonlinear and uncertain control systems. The research of artificial neural network can be divided into three main areas: research on neural network theory, research on neural network application and research on neural network implementation technology. Neural network implementation technology can be divided into hardware realization and virtual realization. Relative to the virtual realization, the hardware realization has better to play the advantages of neural network in fast and large-scale parallel calculation and it is more practical than hardware realization. Thus, hardware implementation technology is an important area of research on neural network.Firstly, this article introduces the Theoretical knowledge, the present situation at home and abroad and the main research direction of neural network. Then, we introduce the main models of neural network, and emphasize on the BP neural network model and algorithm, and summarize the limitations and improvements of BP algorithm. At last, we focus on the hardware implementation technology of artificial neural network, and realize single neural model and Sigmoid’function successfully, and on this basis realize a 2-3-1 BP neural network.