Conference proceeding
Design of the fully connected binary neural network via linear programming
1990 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1094-1097 vol.2
1990
Abstract
An attempt is made to develop an alternative to the Hebbian-hypothesis-based design, using a powerful linear-programming (LP)-based algorithm. The LP-based algorithm attempts to build around each pattern to be stored a ball with a prespecified radius (in the Hamming distance sense) which is the ball of convergence for the pattern: when the network starts as one of the states in the ball, it will eventually converge to the central pattern. The Hopfield model and the sum-of-outer-products (SOOP) design are presented. Calculations are made of the radius of the balls of convergence for any given design. The LP-based algorithm is developed, and examples are presented demonstrating the advantages accrued for the network's retrieval capability through the LP algorithm.< >
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Details
- Title
- Design of the fully connected binary neural network via linear programming
- Creators
- M Kam - Drexel UniversityJ.-C Chow - Drexel UniversityR Fischl - Drexel University
- Publication Details
- 1990 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1094-1097 vol.2
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Other Identifier
- 991019346719904721