# Neural networks learning as a multiobjective optimal control problem.

Mathware and Soft Computing (1997)

- Volume: 4, Issue: 3, page 195-202
- ISSN: 1134-5632

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topKrawczak, Maciej. "Neural networks learning as a multiobjective optimal control problem.." Mathware and Soft Computing 4.3 (1997): 195-202. <http://eudml.org/doc/39108>.

@article{Krawczak1997,

abstract = {The supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.},

author = {Krawczak, Maciej},

journal = {Mathware and Soft Computing},

keywords = {Redes neuronales; Teoría del aprendizaje; Control óptimo; Criterio minimax; Resolución de problemas; supervised learning; multilayer feedforward neural networks},

language = {eng},

number = {3},

pages = {195-202},

title = {Neural networks learning as a multiobjective optimal control problem.},

url = {http://eudml.org/doc/39108},

volume = {4},

year = {1997},

}

TY - JOUR

AU - Krawczak, Maciej

TI - Neural networks learning as a multiobjective optimal control problem.

JO - Mathware and Soft Computing

PY - 1997

VL - 4

IS - 3

SP - 195

EP - 202

AB - The supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.

LA - eng

KW - Redes neuronales; Teoría del aprendizaje; Control óptimo; Criterio minimax; Resolución de problemas; supervised learning; multilayer feedforward neural networks

UR - http://eudml.org/doc/39108

ER -

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