Multi-Sensor Integration Based on a New Quantum Neural Network Model for Land-Vehicle Navigation

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Debao Yuan ◽  
Liangli Cai ◽  
Meng Li ◽  
Chen Liang ◽  
Xiaobo Hou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Sensor Integration ◽  
Vehicle Navigation ◽  
Quantum Neural Network ◽  
Land Vehicle ◽  
Land Vehicle Navigation

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.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

2014 ◽  
Vol 67 (6) ◽  
pp. 967-983 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Binghao Li ◽  
Jingxiang Gao ◽  
Xinglong Tan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Positioning Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Fuzzy Neural

The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

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.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

2004 ◽  
Vol 57 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Jau-Hsiung Wang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Urban Areas ◽  
Low Cost ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Artificial Neural Network Ann ◽  
Signal Fading

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

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.

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