✦ LIBER ✦

The training of neural networks to model manufacturing processes

✍ Scribed by Wimalin Sukthomya; James Tannock

Book ID: 106387540
Publisher: Springer US
Year: 2005
Tongue: English
Weight: 178 KB
Volume: 16
Category: Article
ISSN: 0956-5515
DOI: 10.1007/s10845-005-4823-7

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Neural-network process modeling of a con

Neural-network process modeling of a continuous manufacturing operation

✍ Deborah F. Cook; A.Dale Whittaker 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 599 KB

Neural-network techniques for the development of models of critical parameters in continuous forest products manufacturing processes are described. Predictive models of strength parameters in particleboard manufacturing were developed utilizing both backpropagation and counterpropagation neural netw

Biologically Inspired Models to Train Ne

Biologically Inspired Models to Train Neural Networks

✍ P.J. Lyons 📂 Article 📅 2003 🏛 Springer-Verlag 🌐 English ⚖ 426 KB

Training the brain using neural-network

Training the brain using neural-network models

✍ SKOYLES, JOHN R. 📂 Article 📅 1988 🏛 Nature Publishing Group 🌐 English ⚖ 148 KB

Autoprogressive training of neural netwo

Autoprogressive training of neural network constitutive models

✍ Jamshid Ghaboussi; David A. Pecknold; Mingfu Zhang; Rami M. Haj-Ali 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 251 KB 👁 2 views

Neural network modelling of fermentation

Neural network modelling of fermentation processes

✍ M. Petrova; P. Koprinkova; T. Patarinska; M. Bliznakova 📂 Article 📅 1998 🏛 Springer 🌐 English ⚖ 187 KB

SELECTION OF TRAINING SAMPLES FOR MODEL

SELECTION OF TRAINING SAMPLES FOR MODEL UPDATING USING NEURAL NETWORKS

✍ C.C. CHANG; T.Y.P. CHANG; Y.G. XU; W.M. TO 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 362 KB

One unique feature of neural networks is that they have to be trained to function. In developing an iterative neural network technique for model updating of structures, it has been shown that the number of training samples required increases exponentially as the number of parameters to be updated in