A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

An Identification Method of Library Identifiers Based on Computer Vision

2013 ◽  
Vol 380-384 ◽  
pp. 3534-3537
Author(s):  
Li Ya Liu
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Network Model ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Computation Time ◽  
Principal Component ◽  
Structural Features ◽  
Traditional Methods ◽  
Quantum Neural Network

For traditional methods of library identifies based on the two-dimensional code characteristics, these methods are time consuming and a lot of prior experience is required. A method of library identifies based on computer vision technology is proposed. In this method, a preprocessing, such as image equalization, binarization and wavelet change, is first performed on the acquired library label images. Then on the basis of the structural features of the character, the features of library identifiers are obtained by applying PCA for a principal component analysis. A quantum neural network model is designed to have an optimization analysis and calculation on the extracted features, to avoid the drawbacks which need a lot of prior knowledge for the traditional methods. At the same time, an optimization is carried out for the neural network model saving a large amount of computation time. The experimental results show that a recognition rate, up to 98.13%, is obtained by using this method. With a high recognition speed, the method can meet the actual needs to be applied in a practical system.

A QUANTUM NEURAL NETWORK MODEL

2002 ◽  
Vol 13 (01) ◽  
pp. 75-88 ◽  
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
General Problem ◽  
Learning Algorithms ◽  
Quantum Gates ◽  
Quantum Neural Network ◽  
Classification Tasks

We present the algorithms necessary for the implementation of a quantum neural network with learning and classification tasks. A complete implementation for the classification and learning algorithms is given in terms of unitary quantum gates. Such a quantum neural network can be used to perform complex classification tasks or to solve the general problem of binary mapping.

scholarly journals Wireless Sensor Node Localization Algorithm Based on Particle Swarm Optimization and Quantum Neural Network

2018 ◽  
Vol 14 (10) ◽  
pp. 230 ◽  
Author(s):  
Yulong Liu ◽  
Xiaoming Yu ◽  
Yuhua Hao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Average Distance ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Node Localization ◽  
Swarm Optimization ◽  
Quantum Neural Network

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">Aiming at the problem of node localization in wireless sensor networks, a location algorithm for optimizing distance vector hopping (DV-hop) by constructing a quantum neural network model based on particle swarm optimization (PSO) is proposed. According to the average distance obtained by the traditional DV-HOP and the distance from the measured nodes, the quantum neural network model is constructed, and the average distance is trained by the particle swarm optimization algorithm which would shorten the training time of the traditional artificial neural network and accelerate the convergence speed. By using the proposed model, the optimal mean value is obtained, and the optimization of the DV-HOP algorithm is realized. The simulation results show that compared with the traditional DV-HOP algorithm, the proposed algorithm can reduce the positioning error by about 20%, and the positioning accuracy is significantly improved.</span>

Fault Diagnosis of Coal Electrical Shearer Based on Quantum Neural

2014 ◽  
Vol 574 ◽  
pp. 452-456 ◽  
Author(s):  
Xian Min Ma ◽  
Mei Hui Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Algorithm ◽  
Nonlinear Function ◽  
Theory Analysis ◽  
Quantum Neural Network ◽  
On Line ◽  
Nonlinear Feature

An improved quantum neural network model and its learning algorithm are proposed for fault diagnosis of the coal electrical haulage shearer in order to on line monitor working states of the large mining rotating machines. Based on traditional BP neural network, the three-layer quantum neural network is constructed to combine quantum calculation and neural network for the error correction learning algorithm. According to the information processing mode of the biology neuron and the quantum computing theory, the improved quantum neural network model has the ability of identifying uncertainty in fault data classifications and approximating the nonlinear function for different fault types to monitor the electrical motor voltage, current, temperature, shearer location, boom inclination, haulage speed and direction in the coal electrical cutting machines. The theory analysis and simulation experiment results show that the control performances and the safety reliability of the coal shearer are obviously improved, while the quantum neural network model is applied to the nonlinear feature fault diagnosis of the coal electrical haulage shearer.

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