Analysis and synthesis of a class of neural networks; Variable structure systems with infinite gain
- Dept. of Electrical and Computer Engineering, Univ. of Notre Dame, Notre Dame, IN (US)
In this paper the authors investigate the qualitative properties of a class of neural networks described by a system of first-order ordinary differential equations with discontinuous right hand side. They develop an efficient synthesis procedure (resp., design procedure) for this class of neural networks. The class of systems considered herein may be used as a representation of the analog Hopfield model with the nonlinearities having infinite gain. Also, under appropriate assumptions, the output of the class of systems considered herein may be viewed as representing the behavior of the discrete Hopfield model. Thus their results give insight into the qualitative behavior of the analog as well as the discrete Hopfield models and they provide a means of designing such models. The applicability of the present results is demonstrated by means of several specific examples.
- OSTI ID:
- 5145320
- Journal Information:
- IEEE (Institute of Electrical and Electronics Engineers) Transactions on Circuits and Systems; (USA), Journal Name: IEEE (Institute of Electrical and Electronics Engineers) Transactions on Circuits and Systems; (USA) Vol. 36:5; ISSN 0098-4094; ISSN ICSYB
- Country of Publication:
- United States
- Language:
- English
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