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Journal of Information Science and Engineering, Vol. 27 No. 2, pp. 481-492 (March 2011)

The Linear Quantization Strategy of Quadratic Hebbian-Type Associative Memories and Their Performance Analysis

CHAO-HUI KO, CHING-TSORNG TSAI*,+ AND CHISHYAN LIAW*
Department of Information Management
Hsiuping Institute of Technology
Taichung, 412 Taiwan
*Department of Computer Science
Tunghai University
Taichung, 407 Taiwan

The Quadratic Hebbian-type associative memories have superior performance, but they are more difficult to implement because of their large interconnection values in chips than are the first order Hebbian-type associative memories. In order to reduce the interconnection value for a neural network with M patterns stored, the interconnection value [- M, M] is mapped to [- H, H] linearly, where H is the quantization level. The probability of direct convergence equation of quantized Quadratic Hebbian-type associative memories is derived and the performances are explored. The experiments demonstrate that the quantized network approaches the original recall capacity at a small quantization level. Quadratic Hebbian-type associative memories usually store more patterns; therefore, the strategy of linear quantization reduces interconnection value more efficiently.

Keywords: Hebbian-type associative memories, quadratic Hebbian-type associative memories, linear quantization, interconnection quantization, probability of direct convergence

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Received May 27, 2009; revised August 18, 2009; accepted December 4, 2009.
Communicated by Chin-Teng Lin.
+ Corresponding author.