Algebraic Approach and Optimal Physical Clusterization in Interpolation Problems of Artificial Intelligence

Abstract

Perceptron-type interpolation systems of artificial intelligence are considered. A concept of optimal physical clusterization allows us to divide a second layer of hidden units into the compact sets of units (clusters). Then, an algebraic approach developed for pattern recognition systems may be extended to other systems. To solve the problems of process forecasting, a data sample should be transformed into a single-moment sample.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Artificial Intelligence; Interpolation; Neural Network; Self-Organization

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2003 Springer, All rights reserved.

Publication Date

01 Jan 2003

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