Book Description
Artificial neural networks (ANN), due to their inherent parallelism, potential for fault tolerance, and adaptation through learning, offer an attractive computational paradigm for a variety of applications in computer science and engineering, artificial intelligence, robotics, and cognitive modeling. Despite the success in the application of ANN to a broad range of numeric tasks in pattern classification, control, function approximation, and system identification, the integration of ANN and symbolic computing is only beginning to be explored. This dissertation explores to integrate ANN and some essential symbolic computations for content-based associative symbolic processing.
