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

Alexey G. Ivakhnenko; Gregory A. Ivakhnenko; E. A. Savchenko; Donald C. Wunsch

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

Alexey G. Ivakhnenko
, Gregory A. Ivakhnenko
, E. A. Savchenko
, Donald C. Wunsch

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Pattern Recognition and Image Analysis
State	Published - Jan 1 2003

Keywords

Artificial Intelligence
Interpolation
Neural Network
Self-Organization

Disciplines

Electrical and Computer Engineering

Cite this

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title = "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.",

keywords = "Artificial Intelligence, Interpolation, Neural Network, Self-Organization",

author = "Ivakhnenko, \{Alexey G.\} and Ivakhnenko, \{Gregory A.\} and Savchenko, \{E. A.\} and Wunsch, \{Donald C.\}",

year = "2003",

month = jan,

day = "1",

language = "American English",

journal = "Pattern Recognition and Image Analysis",

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T1 - Algebraic Approach and Optimal Physical Clusterization in Interpolation Problems of Artificial Intelligence

AU - Ivakhnenko, Alexey G.

AU - Ivakhnenko, Gregory A.

AU - Savchenko, E. A.

AU - Wunsch, Donald C.

PY - 2003/1/1

Y1 - 2003/1/1

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Interpolation

KW - Neural Network

KW - Self-Organization

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/248

M3 - Article

JO - Pattern Recognition and Image Analysis

JF - Pattern Recognition and Image Analysis

ER -

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

Abstract

Keywords

Disciplines

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