Modular neural networks for function approximation

Taek Kwon; Hui Cheng; Michael Zervakis

Modular neural networks for function approximation

Taek Kwon, Hui Cheng, Michael Zervakis

Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

In this paper we consider a network that consists of two modules of neural networks, and attempt to establish an optimal utilization of these modules. Such a study is useful for developing efficient modular neural network (MNN) architectures. We propose three modular architectures that implement a complementary relation, in which the deficiency of one module is complemented by the other module. We provide simulation examples for these three architectures.

Original language	English (US)
Pages	11-16
Number of pages	6
State	Published - Dec 1 1994
Event	Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) - St. Louis, MO, USA Duration: Nov 13 1994 → Nov 16 1994

Other

Other	Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)
City	St. Louis, MO, USA
Period	11/13/94 → 11/16/94

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title = "Modular neural networks for function approximation",

abstract = "In this paper we consider a network that consists of two modules of neural networks, and attempt to establish an optimal utilization of these modules. Such a study is useful for developing efficient modular neural network (MNN) architectures. We propose three modular architectures that implement a complementary relation, in which the deficiency of one module is complemented by the other module. We provide simulation examples for these three architectures.",

author = "Taek Kwon and Hui Cheng and Michael Zervakis",

year = "1994",

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AU - Kwon, Taek

AU - Cheng, Hui

AU - Zervakis, Michael

PY - 1994/12/1

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N2 - In this paper we consider a network that consists of two modules of neural networks, and attempt to establish an optimal utilization of these modules. Such a study is useful for developing efficient modular neural network (MNN) architectures. We propose three modular architectures that implement a complementary relation, in which the deficiency of one module is complemented by the other module. We provide simulation examples for these three architectures.

AB - In this paper we consider a network that consists of two modules of neural networks, and attempt to establish an optimal utilization of these modules. Such a study is useful for developing efficient modular neural network (MNN) architectures. We propose three modular architectures that implement a complementary relation, in which the deficiency of one module is complemented by the other module. We provide simulation examples for these three architectures.

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M3 - Paper

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T2 - Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)

Y2 - 13 November 1994 through 16 November 1994

ER -

Modular neural networks for function approximation

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