Neural networks with weight function and application

Author

Niaona, Zhang ; Xiuhe, Lu ; Dejiang, Zhang ; Fang, Chen

Author_Institution

Coll. of Electr. & Electron. Eng., Changchun Univ. of Technol., Changchun, China

fYear

2009

fDate

5-7 Aug. 2009

Firstpage

1273

Lastpage

1277

Abstract

Against these disadvantages like local minimum, slow convergence speed, non-convergence and difficulty in obtaining of global optimal point, the new Neural Networks with Weight Function is proposed in this paper with simple network topology constituted by input layer and output layer only. This network is used in establishing the Energy Consumption Forecasting Model of DAGUSHAN Ore Dressing Plant. According to the production data in actual production process and the gap of these data, different interpolation functions are selected to be the weight functions. Simulation examples show the good performance of this method that little calculation work, with no local minimum and slow convergence problems. Model mentioned above has minor error and the better prediction effect is obtained.

Keywords

convergence; forecasting theory; load forecasting; neural nets; power engineering computing; topology; DAGUSHAN ore dressing plant; energy consumption forecasting model; global optimal point; input layer; interpolation function; local minimum; neural network; nonconvergence; output layer; production data; production process; simple network topology; slow convergence problem; slow convergence speed; weight function; Convergence; Energy consumption; Interpolation; Load forecasting; Network topology; Neural networks; Neurons; Predictive models; Production facilities; Spline; energy consumption forecasting; neural networks; weight function;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2009. ICAL '09. IEEE International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-4794-7

Electronic_ISBN

978-1-4244-4795-4

Type

conf

DOI

10.1109/ICAL.2009.5262770

Filename

5262770