๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Supervised Feature Ranking Using a Genetic Algorithm Optimized Artificial Neural Network.

โœ Scribed by Thy-Hou Lin; Shih-Hau Chiu; Keng-Chang Tsai


Publisher
John Wiley and Sons
Year
2006
Weight
8 KB
Volume
37
Category
Article
ISSN
0931-7597

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Modelling evaporation using an artificia
โœ K. P. Sudheer; A. K. Gosain; D. Mohana Rangan; S. M. Saheb ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 244 KB ๐Ÿ‘ 1 views

## Abstract This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were

Global optimization of a dryer by using
โœ A. Hugget; P. Sรฉbastian; J.-P. Nadeau ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› American Institute of Chemical Engineers ๐ŸŒ English โš– 162 KB ๐Ÿ‘ 2 views

For many optimum design problems, the objectiยฎe function is the result of a complex numerical code and may not be differentiable and explicit. The first aim is to propose a way of solยฎing such complexity on an example problem. A noยฎel and global strategy inยฎolยฎing artificial neural networks and a ge

FAULT DETECTION USING SUPPORT VECTOR MAC
โœ L.B. JACK; A.K. NANDI ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 196 KB

Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical methods to preprocess the vibration signals as input features. ANNs have been shown to be highly successful in this type of application; in comparison, support vector mach

Modelling of structural response and opt
โœ Li, Q. S. ;Liu, D. K. ;Leung, A. Y. T. ;Zhang, N. ;Tam, C. M. ;Yang, L. F. ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 151 KB ๐Ÿ‘ 1 views

This paper proposes an integrated approach to the modelling and optimization of structural control systems in tall buildings. In this approach, an artificial neural network is applied to model the structural dynamic responses of tall buildings subjected to strong earthquakes, and a genetic algorithm