Short-term load forecasting model based on the fusion of PSRT and QCNN
type: Item
instance of: scholarly article
title: Short-term load forecasting model based on the fusion of PSRT and QCNN
zbMATH Open document ID: 1426.81028
DOI: 10.1155/2017/3485182
author: Zhisheng Zhang
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simulation results show that proposed model has good forecasting precision and stability.
Mathematics Subject Classification ID: 81P68
Mathematics Subject Classification ID: 81V35
Mathematics Subject Classification ID: 37N35
zbMATH DE Number: 6971989
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/3485182
OpenAlex ID: W2761293108
cites work: Measuring the strangeness of strange attractors
Recommended article: Q3596021
Recommended article: Short-term load forecasting model based on quantum Elman neural networks
Recommended article: A Short-Term Load Forecasting Method Based on RBF Neural Network and Fuzzy Reasoning
Recommended article: An integrated method based on relevance vector machine for short-term load forecasting
description: scientific article; zbMATH DE number 6971989