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<p><b>Abstract</b>—This paper begins with an overview of several competitive learning algorithms in artificial neural networks, including self-organizing feature maps, focusing on properties of these algorithms important to hardware implementations. We then discuss previously reported digital implementations of these networks. Finally, we report a reconfigurable parallel neurocomputer architecture we have designed using digital signal processing chips and field-programmable gate array devices. Communications are based upon a broadcast network with FPGA-based message preprocessing and postprocessing. A small prototype of this system has been constructed and applied to competitive learning in self-organizing maps. This machine is able to model slowly-varying nonstationary data in real time.</p>
Computer architecture, parallel processing, neurocomputers, field programmable devices, artificial neural networks, competitive learning, self-organizing feature maps.

D. McNeill, G. Rosendahl, H. Card and R. McLeod, "Competitive Learning Algorithms and Neurocomputer Architecture," in IEEE Transactions on Computers, vol. 47, no. , pp. 847-858, 1998.
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