Neural network control and an optoelectronic implementation of a multilayer feedforward neural network

[摘要] Artificial neural networks are a computational paradigm inspired by biological neural systems. By modeling neural networks to a certain degree after their counterparts in nature, it is hoped that they can capture those aspects of biological neural systems that allow them to outperform more conventional processing systems in tasks such as motor control and pattern recognition. A brief overview of neural networks is provided in Item 1, concentrating on those aspects pertinent to the remainder of this thesis.The application of neural networks to control is examined in Item 2. A general control system can be divided into feedforward and feedback components. Specifically, the use of neural networks in learning to generate the feedforward control signal for unknown, potentially nonlinear, plants is examined. A class of learning algorithms applicable to feedforward networks is developed, and their use in learning to control a simulated two-link robotic manipulator is studied.An optoelectronic implementation of a multilayer feedforward neural network, with binary weights and connections, is described in the final part of this thesis. The neurons and connections are implemented electronically on a custom VLSI chip. The pattern and strength of the connections is controlled, through photodetectors placed in the connections, by a pattern of light illuminating the chip. This pattern is read out, in parallel, from an optical disk. Issues concerning parallel readout of information from optical disks are discussed in Item 3, while Item 4 contains a descriptionn of both the design of the Optoelectronic Neural Network Chip (ONNC) and experiments involving the optical disk and neural network chip.

[发布机构] University:California Institute of Technology;Department:Engineering and Applied Science

[关键词] Electrical Engineering