The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Mixed Mode VLSI Implementation of a Neural Associative Memory
Analog Integrated Circuits and Signal Processing
Principal Interconnections in Higher Order Hebbian-Type Associative Memories
IEEE Transactions on Knowledge and Data Engineering
An Efficient Hardware Implementation of Feed-Forward Neural Networks
Applied Intelligence
Image Recognition Processor based on Morphological Associative Memories
CERMA '07 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference
Analysis of quantization effects on high-order function neural networks
Applied Intelligence
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A class of sparsely connected autoassociative morphological memories for large color images
IEEE Transactions on Neural Networks
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Color image associative memory on a class of Cohen-Grossberg networks
Pattern Recognition
An associative memory-based learning model with an efficient hardware implementation in FPGA
Expert Systems with Applications: An International Journal
Computational Intelligence-Based Biometric Technologies
IEEE Computational Intelligence Magazine
A kernel autoassociator approach to pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural associative memory storing gray-coded gray-scale images
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
Gabor wavelet associative memory for face recognition
IEEE Transactions on Neural Networks
Associative memory design for 256 gray-level images using a multilayer neural network
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Improvements of Complex-Valued Hopfield Associative Memory by Using Generalized Projection Rules
IEEE Transactions on Neural Networks
Characteristics of Hebbian-type associative memories having faulty interconnections
IEEE Transactions on Neural Networks
Global Convergence and Limit Cycle Behavior of Weights of Perceptron
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Hebbian-type associative memory is characterized by its simple architecture. However, the hardware implementation of Hebbian-type associative memories is normally complicated when there are a huge number of patterns stored. To simplify the interconnection values of a network, a nonlinear quantization strategy is presented. The strategy takes into account the property that the interconnection values are Gaussian distributed, and divides the interconnection weight values into a small number of unequal ranges accordingly. Interconnection weight values in each range contain information equally and each range is quantized to a value.The equation of probability of direct convergence was derived. The probability of direct convergence of nonlinear quantized networks with a small number of ranges is compatible with their original networks. The effects of linear and nonlinear quantization were also assessed in terms of recall capability, information capacity, and number of bits storing interconnection values saved by quantization. The performance of the proposed nonlinear quantization strategy is better than that of the linear quantization while retaining a recall capability that is compatible with its original network. The proposed approach reduces the number of connection weights and the size of the chip areas of a Hebbian-type associative memory while approximately retaining its performance.