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A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories
May 1993 (vol. 42 no. 5)
pp. 636-640

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.

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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:
Tao Wang, Xinhau Zhuang, XiaoLiang Xing, Xipeng Xiao, "A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories," IEEE Transactions on Computers, vol. 42, no. 5, pp. 636-640, May 1993, doi:10.1109/12.223686
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