Title :
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
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;
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
DOI :
10.1109/ICAL.2009.5262770
