Journal article
On the stability, storage capacity, and design of nonlinear continuous neural networks
IEEE transactions on systems, man, and cybernetics, v 18(1), pp 80-87
01 Feb 1988
Abstract
The stability, capacity, and design of a nonlinear continuous neural network are analyzed. Sufficient conditions for existence and asymptotic stability of the network's equilibria are reduced to a set of piecewise-linear inequality relations that can be solved by a feedforward binary network, or by methods such as Fourier elimination. The stability and capacity of the network is characterized by the post synaptic firing rate function. An N-neuron network with sigmoidal firing function is shown to have up to 3N equilibrium points. This offers a higher capacity than the (0.1-0.2)N obtained in the binary Hopfield network. Moreover, it is shown that by a proper selection of the postsynaptic firing rate function, one can significantly extend the capacity storage of the network.
Metrics
Details
- Title
- On the stability, storage capacity, and design of nonlinear continuous neural networks
- Creators
- Allon Guez - Drexel UniversityVladimir Protopopsecu - Oak Ridge National LaboratoryJacob Barhen - Jet Propulsion Lab
- Publication Details
- IEEE transactions on systems, man, and cybernetics, v 18(1), pp 80-87
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1988M650300007
- Scopus ID
- 2-s2.0-0023847221
- Other Identifier
- 991019173715504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Engineering, Electrical & Electronic