Title :
A new class of neural networks based on approximate identities for approximation and learning
Author :
Turchetti, Claudio ; Conti, M.
Author_Institution :
Dipartimento di Elettronica, Ancona Univ., Italy
Abstract :
Learning a given input-output mapping can be regarded as a problem of approximating a multivariate function. A theoretical framework for approximation, based on sequences of functions named approximate identities, is developed. It is proved that such sequences are able to approximate a generally continuous function with a given error. This leads to a new class of three-layer networks that can efficiently be implemented in analog MOS VLSI
Keywords :
MOS integrated circuits; VLSI; analogue processing circuits; feedforward neural nets; learning (artificial intelligence); analog MOS VLSI; approximate identities; generally continuous function; input-output mapping; learning; multivariate function; neural networks; three-layer networks; Analog circuits; Approximation methods; Convolution; Linear approximation; Multidimensional systems; Neural networks; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.229939
