Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution

Chun Ho Jeon; Yu Jin Cheon; Su Whan Sung; Changkyu Lee; Changkyoo Yoo; Dae Ryook Yang

Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution

Chun Ho Jeon, Yu Jin Cheon, Su Whan Sung, Changkyu Lee, Changkyoo Yoo, Dae Ryook Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A new supervisory training rule for the multilayered feedforward neural network (FNN) using local linearization and analytic optimal solution is proposed. The cause of the nonlinearity of the neural network in the training is pinpointed and the nonlinearity is removed by a local linearization. And, the optimal solution of the linearized FNN minimizing the objective function for the training is analytically derived. The proposed training rule shows the shortest training time among the previous approaches. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of the pH process.

Original language	English
Title of host publication	ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages	3697-3701
Number of pages	5
Publication status	Published - 2009
Event	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan Duration: 2009 Aug 18 → 2009 Aug 21

Publication series

Name	ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

Other	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
Country/Territory	Japan
City	Fukuoka
Period	09/8/18 → 09/8/21

Keywords

Linearization
Neural network
Optimal solution
Training rule

ASJC Scopus subject areas

Information Systems
Control and Systems Engineering
Industrial and Manufacturing Engineering

Cite this

Jeon, C. H., Cheon, Y. J., Sung, S. W., Lee, C., Yoo, C., & Yang, D. R. (2009). Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 3697-3701). Article 5334761 (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).

Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution. / Jeon, Chun Ho; Cheon, Yu Jin; Sung, Su Whan et al.
ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 3697-3701 5334761 (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeon, CH, Cheon, YJ, Sung, SW, Lee, C, Yoo, C & Yang, DR 2009, Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution. in ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings., 5334761, ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings, pp. 3697-3701, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, Japan, 09/8/18.

Jeon CH, Cheon YJ, Sung SW, Lee C, Yoo C, Yang DR. Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 3697-3701. 5334761. (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).

Jeon, Chun Ho ; Cheon, Yu Jin ; Sung, Su Whan et al. / Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 3697-3701 (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).

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AB - A new supervisory training rule for the multilayered feedforward neural network (FNN) using local linearization and analytic optimal solution is proposed. The cause of the nonlinearity of the neural network in the training is pinpointed and the nonlinearity is removed by a local linearization. And, the optimal solution of the linearized FNN minimizing the objective function for the training is analytically derived. The proposed training rule shows the shortest training time among the previous approaches. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of the pH process.

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KW - Optimal solution

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BT - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

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Development of a supervisory training rule for multilayered feedforward neural network using local linearization and analytic optimal solution

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