Nonlinear output error model using a single hidden layer neural network: an application case study

Title:

Nonlinear output error model using a single hidden layer neural network: an application case study

Creator:

Haber, R. E., Alique, A., Ros, S., Alique, J. R., Haber, R. H., and Jiménez, J. E.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:8826fe41-e81a-4770-b4bf-0152ab8bc121
uuid:8826fe41-e81a-4770-b4bf-0152ab8bc121

Subject:

nonlinear output error model, multi-layer perceptron, and machining process

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This work is focused on determninig a nonlinear output error (OE) model, i.e., a dynamic system, by training a two layer neural network with a Levenberg-Marquardt method. Selected as a case study is application of a dynamic model to predict cutting force in machining processes. A model crated by using Artificial Neural Networks (ANN), able to predict the process output, is introduced in order to deal with the characteristics of such an ill-defined process. This model describes the dynamic response of the output before the changes in the process input command (feed rateú and the process parameters (depth of cut). The model provides a sufficiently accurate predition of cutting foce, since the process-dependent specific dynamic properties are adequately reflected.

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 | 2002 Volume:12 | Number:6

Harvested from:

CDK

Metadata only:

false