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ABSTRACT
<p>A novel learning algorithm for a neuron-weighted associative memory (NWAM) is presented. The learning procedure is cast as a global minimization, solved by a gradient descent rule. An analog neural network for implementing the learning method is described. Some computer simulation experiments are reported.</p>
INDEX TERMS
hardware implementation; associative memories; learning algorithm; neuron-weighted associative memory; NWAM; global minimization; gradient descent rule; analog neural network; computer simulation experiments; content-addressable storage; learning (artificial intelligence); neural chips; neural nets.
CITATION

T. Wang, X. Zhuang, X. Xing and X. Xiao, "A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories," in IEEE Transactions on Computers, vol. 42, no. , pp. 636-640, 1993.
doi:10.1109/12.223686
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