WAVELETS AS BASIS FUNCTIONS FOR LOCALIZED LEARNING IN A MULTI-RESOLUTION HIERARCHY

Bhavik R. Bakshi, George Stephanopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

A novel artificial neural network with one hidden layer of nodes, whose basis functions are drawn from a family of orthonormal wavelets, is developed in this paper. Wavelet Networks or Wave-Nets are based on firm theoretical foundations of functional analysis. The good localization characteristics of the basis functions, both in the input and frequency domains, allow hierarchical, multi-resolution learning of input-output maps from experimental data. Furthermore, Wave-Nets allow explicit estimation of global and local prediction error-bounds, and thus lend themselves to a rigorous and transparent design of the network. Computational complexity arguments prove that the training and adaptation efficiency of Wave-Nets is at least an order of magnitude better than other networks. This paper presents the mathematical framework for the development of Wave-Nets and discusses various aspects of their practical implementation. The problem of predicting a chaotic time-series is solved as an illustrative example.

Original languageEnglish (US)
Title of host publicationProceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages140-145
Number of pages6
ISBN (Electronic)0780305590
DOIs
StatePublished - 1992
Event1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States
Duration: Jun 7 1992Jun 11 1992

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Conference

Conference1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/TerritoryUnited States
CityBaltimore
Period6/7/926/11/92

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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