Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

AC Okafor, O Adetona - Applications of Artificial Neural …, 1994 - spiedigitallibrary.org
This paper presents a systematic evaluation of the individual effects of training parameters:
learning rate, momentum rate, number of hidden layer nodes, and processing element's …
被引用次数：5 相关文章

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

A Okafor, O Adetona - spiedigitallibrary.org
Artificial neural networks have been shown to have a lot of potential as a means of
integrating multi-sensor signals for on-line real time monitoring of machining processes …

Multi-Sensor Integration using Neural Networks for Predicting Quality Characteristics of End-Milled Parts-Part I: Individual Effects of Training Parameters

AC Okafor, O Adetona - 1994 - scholarsmine.mst.edu
Artificial neural networks have been shown to have a lot of potential as a means of
integrating multi-sensor signals for on-line real time monitoring of machining processes …

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

AC Okafor, O Adetona - Applications of Artificial Neural …, 1994 - ui.adsabs.harvard.edu
This paper presents a systematic evaluation of the individual effects of training parameters:
learning rate, momentum rate, number of hidden layer nodes, and processing element's …