Artificial intelligence methods used for predicting traction power consumption

Title:

Artificial intelligence methods used for predicting traction power consumption

Creator:

Sadil, Jindřich

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9d170786-a6ac-4469-a194-dc9c01809e9d
uuid:9d170786-a6ac-4469-a194-dc9c01809e9d

Subject:

Predictive modeling, neural networks, genetic algorithms, control systems, traction power, and electrified railway

Type:

model:article and TEXT

Format:

jiný

Description:

In this paper some remarks on predictive modeling of traction power consumption and their use in intelligent control systems are stated. Special emphasis is put on discussing neural networks and genetic algorithms for such models described in the second Chapter. In the third Chapter, significant applications of neural networks and genetic algorithms in area of power consumption and train diagram are stated. A methodology of model development and assessment is presented in Chapter 4. In Chapter 5 there are up to now results of the author's traction power consumption prediction coming out from artificial neural network predictive models developed in Mathematica SW environment. Finally, summary and further work are stated in the last Chapter.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:1

Harvested from:

CDK

Metadata only:

false