Computing Higher Order Derivatives in Universal Learning Networks

Kotaro Hirasawa; Jinglu Hu; Masanao Ohbayashi; Junichi Murata

doi:10.20965/jaciii.1998.p0047

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JACIII Vol.2 No.2 pp. 47-53

doi: 10.20965/jaciii.1998.p0047

(1998)

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Computing Higher Order Derivatives in Universal Learning Networks

Kotaro Hirasawa, Jinglu Hu, Masanao Ohbayashi, and Junichi Murata

Department of Electrical and Electronic Systems Engineering, Kyushu University. Hakozaki, Fukuoka 812-81, Japan

Received:

October 27, 1997

Accepted:

March 3, 1998

Published:

April 20, 1998

Keywords:

learning network, neural network, higher order derivative, backward propagation, forward propagation

Abstract

This paper discusses higher order derivative computing for universal learning networks that form a super set of all kinds of neural networks. Two computing algorithms, backward and forward propagation, are proposed. Using a technique called “local description” expresses the proposed algorithms very simply. Numerical simulations demonstrate the usefulness of higher order derivatives in neural network training.

Cite this article as:

Kotaro Hirasawa, Jinglu Hu, Masanao Ohbayashi, and Junichi Murata, “Computing Higher Order Derivatives in Universal Learning Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.2, No.2, pp. 47-53, 1998.

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