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Neural Networks and Knowledge Engineering
June 1991 (vol. 3 no. 2)
pp. 185-192

A brief overview is given of the nature of present day artificial neural net computing and the authors emphasize the theme that in this mode of computing, knowledge is not represented symbolically, but in the form of distributed processing and localized decision rules. The authors propose that in neural-net computing, the processing is the representation. In other words, the very nature of the processing encodes the knowledge. There is no place and no need for a separate body of global rules to be used by the network for inferencing. If rules exist at all, they are in the nature of local processing steps carried out at individual processors in response to stimuli from other neurons. The authors develop this theme together with a theme which is closely related to it. The second notion is that neural networks may also be thought of and implemented in terms of heterogeneous networks, rather than always or only in terms of massive arrays of identical elemental processors.

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Index Terms:
knowledge engineering; artificial neural net computing; distributed processing; localized decision rules; global rules; local processing steps; neurons; heterogeneous networks; identical elemental processors; distributed processing; knowledge engineering; neural nets
Citation:
Y.-H. Pao, D.J. Sobajic, "Neural Networks and Knowledge Engineering," IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 2, pp. 185-192, June 1991, doi:10.1109/69.87998
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