Journal article
Stochastic noise Process enhancement of Hopfield neural networks
IEEE transactions on circuits and systems. II, Express briefs, v 52(4)
Apr 2005
Abstract
Hopfield neural networks (HNN) are a class of densely connected single-layer nonlinear networks of perceptrons. The network's energy function is defined through a learning procedure so that its minima coincide with states from a predefined set. However, because of the network's nonlinearity, a number of undesirable local energy minima emerge from the learning procedure. This has shown to significantly effect the network's performance. In this brief, we present a stochastic process-enhanced binary HNN. Given a fixed network topology, the desired final distribution of states can be reached by modulating the network's stochastic process. We design this process, in a computationally efficient manner, by associating it with stability intervals of the nondesired stable states of the network. Our experimental simulations confirm the predicted improvement in performance.
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Details
- Title
- Stochastic noise Process enhancement of Hopfield neural networks
- Creators
- V Pavlovic - Dept. of Comput. Sci., Rutgers Univ., USAD Schonfeld - Dept. of Comput. Sci., Rutgers Univ., USAG Friedman
- Publication Details
- IEEE transactions on circuits and systems. II, Express briefs, v 52(4)
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000228193100009
- Scopus ID
- 2-s2.0-17844408677
- Other Identifier
- 991014877834304721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic