Paper
16 September 1992 Neural networks for matched filter selection and synthesis
Peter T. Kazlas, Peter T. Monsen
Author Affiliations +
Abstract
Neural networks are investigated for real-time matched filter selection in an optical correlator system. The input to the neural network is a sample space of the optical Fourier transform and the output is a pointer to the correct matched filter. Smaller feedforward network models were ported to analog neural hardware. Simulation and hardware results are discussed. Some architecture recommendations are suggested, specifically an associative memory for filter synthesis.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter T. Kazlas and Peter T. Monsen "Neural networks for matched filter selection and synthesis", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140049
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Neurons

Optical filters

Content addressable memory

Fourier transforms

Analog electronics

Optical correlators

RELATED CONTENT

Multifunctional hybrid optical/digital neural net
Proceedings of SPIE (August 01 1990)
Optical Implementation Of Programmable Neural Networks
Proceedings of SPIE (June 29 1989)
Correlator self-calibration method
Proceedings of SPIE (March 15 1996)
Novel association model optimized by neural net
Proceedings of SPIE (August 19 1993)
Weight discretization paradigm for optical neural networks
Proceedings of SPIE (August 01 1990)
Large-capacity neural nets for scene analysis
Proceedings of SPIE (September 16 1992)

Back to Top