Research Article
PSO Swarm intelligence technique to optimized ANN for demand forcasting
@INPROCEEDINGS{10.4108/eai.28-6-2020.2297944, author={Daham Matrood and Muna Sedeeq}, title={PSO Swarm intelligence technique to optimized ANN for demand forcasting}, proceedings={Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020}, publisher={EAI}, proceedings_a={IMDC-SDSP}, year={2020}, month={9}, keywords={swarm intelligence optimization artificial neural network backpropagation}, doi={10.4108/eai.28-6-2020.2297944} }- Daham Matrood
Muna Sedeeq
Year: 2020
PSO Swarm intelligence technique to optimized ANN for demand forcasting
IMDC-SDSP
EAI
DOI: 10.4108/eai.28-6-2020.2297944
Abstract
With the expansion and development of artificial intelligence algorithms and the use of swarm intelligence and artificial neural networks extensively in solving many complex problems recently. In this research, one of the swarm intelligence algorithms was used, which is represented by the algorithm of the Particle swarm optimization (PSO), and then combined it with one of the algorithms of artificial neural networks, that represented by the algorithm of error back propagation neural network EBPNN, in order to solve the problem of forecasting the demand. And the data that was used in this research was prepared by the general company for prepared clothes and the northern general company for cement represented by the weekly demand data for cement and towels. And the method of the combined PSO with EBPNN obtained the better performance than the standard back propagation neural network algorithm.
