Identification of Infinite Dimensional Systems via Adaptive Wavelet Neural Networks

Abstract

We consider identification of distributed systems via adaptive wavelet neural networks (AWNNs). We take advantage of the multiresolution property of wavelet systems and the computational structure of neural networks to approximate the unknown plant successively. A systematic approach is developed in this paper to find the optimal discrete orthonormal wavelet basis with compact support for spanning the subspaces employed for system identification. We then apply backpropagation algorithm to train the network with supervision to emulate the unknown system. This work is applicable to signal representation and compression under the optimal orthonormal wavelet basis in addition to autoregressive system identification and modeling. We anticipate that this work be intuitive for practical applications in the areas of controls and signal processing.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1993
Accession Number
ADA454923

Entities

People

  • J. S. Baras
  • Yan Zhuang

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Identification
  • Information Operations
  • Neural Networks
  • Signal Processing
  • Universities

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks