Study of a New Wavelet Neural Network of Image Compression Simulation

2012 ◽  
Vol 490-495 ◽  
pp. 623-627
Author(s):  
Xue Zhang Zhao ◽  
Qun Qi
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Bp Neural Network ◽  
Local Area ◽  
Wavelet Neural Network ◽  
Convergence Speed ◽  
Good Time ◽  
Time Frequency ◽  
Hidden Layer ◽  
Self Learning

In the practical need in order to make the most effective image compression in this paper, a new image compression used wavelet neural network model, and gives the corresponding calculation formula and algorithm procedures, By using wavelet transform good time-frequency local area on the characteristics and neural network self-learning function characteristics, overcome traditional BP neural network of hidden-layer points are difficult to be determined and the convergence speed is slow and easy to converge to a local minimum points shortcomings. The results of the simulation experiment prove wavelet neural network image compression characteristic and the convergence speed are much better than traditional BP neural network, and show that the algorithm is effective and feasible.

The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network

2010 ◽  
Vol 37-38 ◽  
pp. 1581-1584
Author(s):  
Xin Yin ◽  
Yuan Peng Liu
Keyword(s):  
Neural Network ◽  
Wavelet Transformation ◽  
Wavelet Neural Network ◽  
Ultrasonic Signal ◽  
Good Time ◽  
Signal Identification ◽  
Time Frequency ◽  
Learning Function ◽  
Nickel Based Superalloy ◽  
Self Learning

By using the good time-frequency localized nature of the wavelet transformation and self-learning function of the traditional artificial neural network, this paper constructed a wavelet neural network model for the blemish signals in ultrasonic testing of the nickel-based superalloy GH4169, and it could recognize types of the blemish signals. The results show that the method is effective in fault diagnosis. Finally the article has confirmed its feasibility and superiority.

The Design of Image Compression with BP Neural Network Based on the Dynamic Adjusting Hidden Layer Nodes

2012 ◽  
Vol 433-440 ◽  
pp. 3797-3801 ◽  
Author(s):  
Jing Li ◽  
Xin Hui Wu ◽  
Chang Hai Qin ◽  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Bp Neural Network ◽  
Training Image ◽  
Compression Algorithms ◽  
Training Time ◽  
Training Images ◽  
Learning Speed ◽  
Dispersion Degree ◽  
Hidden Layer

Aiming at the image compression algorithms with the used BP neural network ,they have inherent defects of poor universality and long training time, a model of the dynamic adjusting hidden layer nodes of BP neural network is designed. According to the training image, using the correlation coefficient and dispersion degree of the same hidden layer’s nodes, we cut and delete some no nodes, this algorithm not only can improve learning speed effectively but also has certain generalization ability, and can complete the task of no- training images compression through experiments.

Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 79-85 ◽  
Author(s):  
Feng Yu ◽  
Zhi Qing Wang ◽  
Xiao Zhong Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Learning Ability ◽  
Time Frequency ◽  
Proposed Model ◽  
Real Coded Genetic Algorithm ◽  
Self Learning ◽  
Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

The Slope Deformation Forecast Model Based on Kalman Filter and Wavelet Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 323-327
Author(s):  
Bing Jun Shi ◽  
Yong Fen Ruan ◽  
Qi Li ◽  
Yong Hong Wu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Geotechnical Engineering ◽  
Forecast Model ◽  
Wavelet Neural Network ◽  
Slope Deformation ◽  
Time Frequency ◽  
Learning Function ◽  
Time Frequency Localization ◽  
Self Learning

Deformation is the macroscopic index for the structure of geotechnical engineering, it is important for the design and construction of geotechnical engineering to monitor the deformation and analyze the monitored data. Kalman filter can enhance the effectiveness of the monitored data and wavelet neural network has the favorable time－frequency localization features and self－learning function. Firstly, the monitored data has been filtered by Kalman filter, and then a deformation forecast model will be established by means of combining with neural network wavelet to predict the deformation of actual engineering. The result shows that the forecast model is successful and effective to forecast the slope deformation.

High-Power AC Servo System Identification Research Based on Wavelet Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 997-1002 ◽  
Author(s):  
Run Min Hou ◽  
Rong Zhong Liu ◽  
Yuan Long Hou ◽  
Qiang Gao
Keyword(s):  
Neural Network ◽  
System Identification ◽  
High Power ◽  
Servo System ◽  
Wavelet Neural Network ◽  
Good Time ◽  
Approximation Accuracy ◽  
Ac Servo System ◽  
Time Frequency ◽  
Ac Servo

As a result of the non-linear characteristics and the uncertain disturbances in high-power AC servo system, it is difficult to construct an accurate mathematical model. In order to solve this problem, this article proposes a system identification method based on wavelet neural network. It makes full use of the advantages of the wavelet which combines neural network good time-frequency localization property and volatility of wavelet function and the nonlinear mapping capacity, self-learning and adaptive capacity of neural networks to solve the problem of non-unique RBF neural network approximation function expression. The simulation results show that the convergence rate, robustness and approximation accuracy of this method are better than the traditional neural network.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

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