Knowledge based dynamic pattern recognition: the recognition of dynamic patterns from minimial [i.e. minimal] examples

This thesis introduces a dynamic recognition neural network model (DRNNM) that provides a theoretical basis for the resolution of a number of pattern recognition problems. These problems consist of: The Binding Problem; The Correspondence Problem; The Learning Complexity Problem and The Knowledge Transference and Extension Problem. The Thesis also addresses related issues, such as: recognition with scarce training resources; dynamic feature extraction and a methodology for reducing learning conflict or crosstalk.