Computing Higher Order Derivatives in Universal Learning Networks | Zendy

Kotaro Hirasawa | Zendy; Jinglu Hu | Zendy; Masanao Ohbayashi | Zendy; Junichi Murata | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Computing Higher Order Derivatives in Universal Learning Networks

Author(s) -

Kotaro Hirasawa,

Jinglu Hu,

Masanao Ohbayashi,

Junichi Murata

Publication year - 1998

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.1998.p0047

Subject(s) - computer science , artificial neural network , order (exchange) , set (abstract data type) , artificial intelligence , theoretical computer science , algorithm , finance , economics , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research