Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification

2012 ◽  
Vol 605-607 ◽  
pp. 2265-2269
Author(s):  
Rui Kun Gong ◽  
Ya Nan Zhang ◽  
Chong Hao Wang ◽  
Li Jing Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Bp Neural Network ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Training Set ◽  
Identification Method ◽  
Compound Model ◽  
Image Definition

First, the background, significance and general implementation of the image definition identification are introduced. Then, basic theory of wavelet transform and neural network is expounded. An identification method of image definition based on the composite model of wavelet analysis and neural network is suggested．The two—dimensional discrete wavelet transformation is used to filter image signal and extract its brim character which is input into BP neural network for identification. 4 layers of BP neural network are constructed to perform image definition identification. The compound model is first trained by 90 images from the training set, and then is tested by 87 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

Image Identification Based on the Compound Model of Wavelet Transform and RBF Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 4152-4155 ◽  
Author(s):  
Rui Kun Gong ◽  
Ping Ting Liu ◽  
Yu Han Gong ◽  
Chong Hao Wang
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Evaluation Method ◽  
Recognition Rate ◽  
Composite Model ◽  
Steepest Descent Method ◽  
Variable Step Size ◽  
Identification Method ◽  
Compound Model ◽  
Image Definition

The image definition identification method based on the composite model of wavelet transform and neural networks is stronger in image edge character extraction, nonlinear process, self-adapted study and pattern recognition. The paper puts forward an evaluation method of image definition based on the focusing mechanism of simulating persons eyes by neural networks and on the composite model of wavelet transformation and neural networks. The wavelet component statistics obtained by the wavelet transform are taken as the inputs of the 5 layer RBF neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step size to adjust the network weights. The compound model is first trained by 75 images from the training set, and then is tested by 102 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals A Hybrid Method of Face Detection Based on Feature Optimization and Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 8998-9002
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Face Detection ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Discrete Wavelet ◽  
Facial Image ◽  
Feature Optimization ◽  
Bp Neural Network Model

The advancement of digital technology needs biometric security systems. Face detection plays an essential role in the security of digital devices. The detection of a face based on the lower content of the facial image for the processing of detection. In this paper modified the BP Neural Network Model for the detection of the human face. The modification of face detection algorithms incorporates feature optimization. The feature optimization process reduces the distorted features of the facial image. The optimized features of facial image enhance the performance of face detection for the optimization of features used glowworm optimization algorithms. The glowworm optimization algorithm is a dynamic population-based search technique. The concept of glowworm is a neighbor’s selection of worms based on the process of lubrification. For feature extraction we use discrete wavelet transform. The discrete wavelet transform function drives the features component in terms of low frequency and high frequency of facial image. The proposed algorithm simulated in MATLAB software and used a reputed facial image dataset from CSV300. Our experimental results show a better detection rate instead of the BP neural network model.

scholarly journals Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 142 ◽  
Author(s):  
Qiongfang Yu ◽  
Yaqian Hu ◽  
Yi Yang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Supply ◽  
Distribution Systems ◽  
Deep Neural Network ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Training Set ◽  
Test Set ◽  
Power Distribution System

The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.

scholarly journals Image Fusion Based on Wavelet Transformation

2020 ◽  
Vol 9 (5) ◽  
pp. 473-477
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Processing Application ◽  
Pulse Coupled Neural Network ◽  
Image Processing Application

The article based totally on the MATLAB software program simulation was carried out on the image fusion; to design and develop a MATLAB based image processing application for fusing two images of the similar scene received through other modalities. The application is required to use Discrete Wavelet Transform (DWT) and Pulse Coupled Neural Network (PCNN) techniques. The comparison is to be performed on the results obtained on the above mentioned techniques.

scholarly journals Recognition of MIR Data of Semen Armeniacae Amarum and Semen Persicae Using Discrete Wavelet Transformation and BP-Artificial Neural Network

2012 ◽  
Vol 27 ◽  
pp. 253-264 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Peng Yu ◽  
Chang-Shun Wu ◽  
Jia-Ni Shou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transformation ◽  
Feature Vector ◽  
Recognition Rate ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Artificial Neural ◽  
The Difference

Horizontal attenuation total reflection-Fourier transformation infrared spectroscopy (HATR-FT-IR) is used to measure the Mid-IR (MIR) of semen armeniacae amarum and its confusable varieties semen persicae. In order to extrude the difference between semen armeniacae amarum and semen persicae, discrete wavelet transformation (DWT) is used to decompose the MIR of semen armeniacae amarum and semen persicae. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of semen armeniacae amarum and semen persicae’s MIR, five feature regions are determined at every spectra band by selecting two scales in the DWT domain. Thus, ten feature parameters form the feature vector. The feature vector is input to the back-propagation artificial neural network (BP-ANN) to train so as to accurately classify the semen armeniacae amarum and semen persicae. 100 couples of MIR are used to train and test the proposed method, where 50 couples of data are used to train samples and other 50 couples of data are used to test samples. Experimental results show that the accurate recognition rate between semen armeniacae amarum and semen persicae is averaged 99% following the proposed method.

Analysis of Back Propagation Neural Network Method for Heart Disease Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 45-54
Author(s):  
Amit K. Gupta ◽  
Ajay Agarwal ◽  
Ruchi Rani Garg
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Wavelet Transform ◽  
Bp Neural Network ◽  
Heart Diseases ◽  
Recognition Rate ◽  
P Wave ◽  
Experimental Result ◽  
Qrs Complex ◽  
T Wave

ECG is the recording of the electrical activity of the heart, and has become one of the most important tools in the diagnosis of heart diseases. ECG signal is shaped by P wave, QRS complex, and T wave. In the normal ECG beat, the main parameters including shape, duration, R-R interval and relationship between P wave, QRS complex, and T wave components are inspected. Any change in these parameters indicates an illness of the heart. This article introduces an electrocardiogram (ECG) pattern recognition method based on wavelet transform and standard BP neural network classifier. Experiment analyzes wavelet transform of ECG to extract the maximum wavelet coefficients of multi-scale firstly. This article then inputs them into BP to classify for different kinds of ECGs. The experimental result shows that the standard BP neural network classifier's overall pattern recognition rate is well.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

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