✦ LIBER ✦

Nonlinear experimental design using Bayesian regularized neural networks

✍ Scribed by Matthew C Coleman; David E Block

Publisher: American Institute of Chemical Engineers
Year: 2007
Tongue: English
Weight: 551 KB
Volume: 53
Category: Article
ISSN: 0001-1541
DOI: 10.1002/aic.11175

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Comparison of experimental designs using

Comparison of experimental designs using neural networks

✍ Yun Lin; Zisheng Zhang; Jules Thibault 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 123 KB

## Abstract Experimental designs were compared using stacked‐layer feed‐forward neural networks. Several traditional three‐level designs and uniform designs were investigated using three‐factor linear and nonlinear models. The prediction error was found to be inversely proportional to the number of

Microstrip antenna design using artifici

Microstrip antenna design using artificial neural networks

✍ Vandana Vikas Thakare; Pramod Singhal 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 295 KB

Chain gear design using artificial neura

Chain gear design using artificial neural networks

✍ Ihsan Toktas; Hudayim Basak 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 581 KB

## Abstract In this study, the analytical calculation and analysis for the data corresponding to these gears have been made to obtain alternative test and training data sets to be used at the artificial neural networks(ANNs) with the constraints and requirements for the design of chain gears of pow

Microstrip filter design using FDTD and

Microstrip filter design using FDTD and neural networks

✍ M. G. Banciu; E. Ambikairajah; R. Ramer 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 226 KB

## Abstract A new design technique using the FDTD method and neural networks is applied to a microstrip filter. The total design time is reduced by two means. First, an iterative ARMA signal estimation technique is utilized to reduce the computation time for each FDTD run. Second, the number of FDT

Prediction of chiral separations using a

Prediction of chiral separations using a combination of experimental design and artificial neural networks

✍ Vlastimil Dohnal; Marta Farková; Josef Havel 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 208 KB 👁 2 views

In this work the advantages of using artificial neural networks (ANNs) combined with experimental design (ED) to optimize the separation of amino acids enantiomers, with ␣-cyclodextrin as chiral selector, were demonstrated. The results obtained with the ED-ANN approach were compared with those of ei

Modeling and design of printed antennas

Modeling and design of printed antennas using neural networks

✍ Yamina Tighilt; Farid Bouttout; Abdelhafid Khellaf 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 305 KB 👁 1 views

A single neural network is developed to model the resonant frequency of rectangular patch printed on uniaxially anisotropic substrate with air gap using effective parameters in conjunction with spectral dyadic Green's function. Also, the strength of ANN models in antenna design is demonstrated by co