Design of bidirectional associative memories based on the perceptron training technique

Author

Salih, Ismail ; Smith, Stanley H. ; Liu, Derong

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Stevens Inst. of Technol., Hoboken, NJ, USA

Volume

5

fYear

1999

fDate

1999

Firstpage

355

Abstract

Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results a specific example is considered

Keywords

content-addressable storage; pattern recognition; perceptrons; recurrent neural nets; bidirectional associative memories; feedback neural networks; linear inequalities; pattern recognition; perceptron training technique; synthesis approach; Algorithm design and analysis; Associative memory; Equations; Magnesium compounds; Network synthesis; Neural networks; Neurofeedback; Neurons; Pattern recognition; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-5471-0

Type

conf

DOI

10.1109/ISCAS.1999.777582

Filename

777582