Issue No. 05 - May (1993 vol. 42)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/12.223686
<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>
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.
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.