A Class of Neural Networks Based on Approximate Identity for Analog IC's Hardware Implementation

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概要

• 論文の詳細を見る

• Artificial Neural Networks (ANN's) that are able to learn exhibit many interesting features making them suitable to be applied in several fields such as pattern recognition, computer vision and so forth. Learning a given input-output mapping can be regarded as a problem of approximating a multivariate function. In this paper we will report a theoretical framework for approximation, based on the well known sequences of functions named approximate identities. In particular, it is proven that such sequences are able to approximate a generally continuous function to any degree of accuracy. On the basis of these theoretical results, it is shown that the proposed approximation scheme maps into a class of networks which can efficiently be implemented with analog MOS VLSI or BJT integrated circuits. To prove the validity of the proposed approach a series of results is reported.

• 社団法人電子情報通信学会の論文
• 1994-06-25

著者

• Orcioni S

  Univ. Ancona Ancona Ita

• Conti M

  Univ. Ancona Ancona Ita

• Orcioni Simone

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

• CONTI Massimo

  Dipartimento di Elettronica ed Automatica, University of Ancona

• TURCHETTI Claudio

  Dipartimento di Elettronica ed Automatica, University of Ancona

• Conti M

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

• Turchetti Claudio

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

関連論文

• An Analog CMOS Approximate Identity Neural Network with Stochastic Learning and Multilevel Weight Storage
• A Class of Neural Networks Based on Approximate Identity for Analog IC's Hardware Implementation
• Artificial neural networks as approximators of stochastic processes

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