DocumentCode :
2614357
Title :
Analysis and synthesis of a class of neural networks with sparse interconnections
Author :
Liu, Derong ; Michel, A.N.
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
fYear :
1993
fDate :
3-6 May 1993
Firstpage :
2596
Abstract :
The authors present results for the analysis and synthesis of a class of neural networks with sparse or partial interconnecting structure. The, design procedure guarantees to synthesize neural networks with arbitrarily predetermined sparse interconnection structures and to store any given set of desired bipolar patterns as memories. It is shown that a sufficient condition for the existence of a design for neural networks with sparse interconnection is self-feedback for every neuron in the network. Several specific examples are included to demonstrate the applicability of the methodology
Keywords :
recurrent neural nets; bipolar patterns; neural networks; partial interconnecting structure; self-feedback; sparse interconnections; Artificial neural networks; Associative memory; Equations; Intelligent networks; Network synthesis; Neural networks; Neurofeedback; Neurons; Sparse matrices; Sufficient conditions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1281-3
Type :
conf
DOI :
10.1109/ISCAS.1993.394297
Filename :
394297
Link To Document :
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