Ho-Kashyap advanced pattern-recognition heteroassociative processors

We review different categories of associative processors with attention to the properties of their key and recollection vectors, the test procedures to be used and the performance measures to be used to compare various associative processors. We review new pseudoinverse and Ho-Kashyap associative processors and robust versions of each. Q uantitative data is presented on the performance of these new pattern recognition associative processors. In all cases we show significant improvement over prior data with M >> N (M is the number of key/recollection vectors pairs stored and N is the dimensionality of the input key vector). Quantization of the number of analog levels and comparisons of various recollection vector encodings are considered.

[1]  D Casasent,et al.  Key and recollection vector effects on heteroassociative memory performance. , 1989, Applied optics.

[2]  David Casasent,et al.  A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects , 1990, Other Conferences.

[3]  David Casasent,et al.  Iterative Fisher/minimum-variance optical classifier , 1990, Pattern Recognit..

[4]  B V Kumar,et al.  Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm. , 1986, Applied optics.

[5]  David Casasent.  Optical morphological processors , 1990, Optics & Photonics.

[6]  D P Casasent,et al.  Ho-Kashyap optical associative processors. , 1990, Applied optics.

[7]  David Casasent,et al.  Ho-Kashyap content-addressable associative processors , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[8]  David Casasent,et al.  Ho-Kashyap Associative Processors , 1989, Other Conferences.

[9]  P J de Groot,et al.  Reconfigurable bipolar analog optical crossbar switch. , 1989, Applied optics.

[10]  D Casasent,et al.  Updating optical pseudoinverse associative memories. , 1989, Applied optics.

[11]  David Casasent,et al.  Optical laboratory realization of a symbolic production system , 1990, Defense, Security, and Sensing.

[12]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[13]  George G. Lendaris,et al.  Diffraction-pattern sampling for automatic pattern recognition , 1970 .