𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Acceleration by prediction for error back-propagation algorithm of neural network

✍ Scribed by Arihiro Kanda; Satoshi Fujita; Tadashi Ae


Publisher
John Wiley and Sons
Year
1994
Tongue
English
Weight
687 KB
Volume
25
Category
Article
ISSN
0882-1666

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

This paper proposes a speed improvement of the error back propagation algorithm, which is employed widely in the multilayered neural network, by introducing the prediction. The idea is to realize a larger acceleration by introducing the differential factor for the moment terms in the error back‐propagation algorithm.


πŸ“œ SIMILAR VOLUMES


Back-propagation neural network for long
✍ Tsong-Lin Lee πŸ“‚ Article πŸ“… 2004 πŸ› Elsevier Science 🌐 English βš– 261 KB

During the recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, explor

Error prediction for neural networks by
✍ Thomas Feuring; Wolfram-M. Lippe πŸ“‚ Article πŸ“… 1998 πŸ› John Wiley and Sons 🌐 English βš– 105 KB πŸ‘ 1 views

Ïn classical ''crisp'' neural networks the output cannot be estimated for arbitrary input data. This situation can be overcome if fuzzy neural nets are trained with fuzzy data. These ''continuous'' data often better describe certain situations. Because fuzzy neural networks map fuzzy numbers to fuzz

Prediction of fatigue life for spot weld
✍ Jung Me Park; Hong Tae Kang πŸ“‚ Article πŸ“… 2007 πŸ› Elsevier Science βš– 228 KB

In this research, the authors developed back-propagation neural networks (BNNs) to predict the fatigue life of spot welds subjected to various geometric factors and loading conditions. This paper described the developing procedures of the BNNs in detail for the spot weld fatigue. Then, the BNNs deve