Time series prediction by parallel feedforward process neural network with time-varied input and output

Title:

Time series prediction by parallel feedforward process neural network with time-varied input and output

Creator:

Gang , Ding and Shisheng, Zhong

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:5595bb5a-33db-4a76-bf7b-b17cbe518f8d
uuid:5595bb5a-33db-4a76-bf7b-b17cbe518f8d

Subject:

Parallel feedforward process neural network (PFPNN), time series, orthogonal basis function, aircraft engine condition monitoring, and learning algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Time series prediction plays an important role in engineering applications. Artificial neural networks seem to be a useful tool to solve these problems. However, in real engineering, the inputs and outputs of many complicated systems are time-varied functions. Conventional artificial neural networks are not suitable to predicting time series in these systems directly. In order to overcome this limitation, a parallel feedforward process neural network (PFPNN) is proposed. The inputs and outputs of the PFPNN are time-varied functions, which makes it possible to predict time series directly. A corresponding learning algorithm for the PFPNN is developed. To simplify this learning algorithm, appropriate orthogonal basis functions are selected to expand the input functions, output functions and network weight functions. The effectiveness of the PFPNN and its learning algorithm is proved by the Mackey-Glass time series prediction. Finally, the PFPNN is utilized to predict exhaust gas temperature time series in aircraft engine condition monitoring, and the simulation test results also indicate that the PFPNN has a faster convergence speed and higher accuracy than the same scale multilayer feedforward process neural network.

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 | 2005 Volume:15 | Number:2

Harvested from:

CDK

Metadata only:

false