The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

2011 ◽  
Vol 121-126 ◽  
pp. 4259-4264 ◽  
Author(s):  
Jian Hui Wang ◽  
Na Chen ◽  
Qian Xiao ◽  
Jian You Xu ◽  
Shu Sheng Gu
Keyword(s):  
Neural Network ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Signal Denoising ◽  
Semg Signal ◽  
Filter Model ◽  
The Neural Network ◽  
Filtering Effect

For the large computation workload of the adaptive filter algorithm and the low filtering speed of the adaptive filter model based on wavelet transform, a wavelet-based neural network adaptive filter model is constructed in this paper. As the neural network has the capacity of distributed storage and fast self-evolution, Hopfield neural network is used to implement adaptive filtering algorithm LMS, so as to increase the computing speed. The model applied to sEMG signal denoising can achieve a better filtering effect.

Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system

2011 ◽  
Vol 17 (13) ◽  
pp. 2007-2014 ◽  
Author(s):  
Jianjun Yao ◽  
Xiancheng Wang ◽  
Shenghai Hu ◽  
Wei Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Servo System ◽  
Inverse Model ◽  
Lms Algorithm ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
The Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

Simultaneous Process Mean and Variance Monitoring Using Wavelet Transform and Probabilistic Neural Network

2012 ◽  
Vol 157-158 ◽  
pp. 11-15 ◽  
Author(s):  
Shao Xiong Wu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Input Data ◽  
Probabilistic Neural Network ◽  
Process Mean ◽  
Mean And Variance ◽  
Simultaneous Process ◽  
The Mean ◽  
Simulation Results

A real-time WPNN-based model was present for the simultaneous recognition of both mean and variance CCPs. In the modeling of structure for patterns recognition, the combined wavelet transform with probabilistic neural network (WPNN) was proposed. Input data was decomposed by wavelet transform into several detail coefficients and approximations. The approximation obtained and energy of every lever detail coefficients was for the input of PNN. The simulation results shows that it can recognize each pattern of the mean and variance CCPs accurately, which can be used in simultaneous process mean and variance monitoring.

Power System Fault Diagnosis Based on Wavelet Transform and Neural Networks

2014 ◽  
Vol 705 ◽  
pp. 255-258 ◽  
Author(s):  
Jun Liu ◽  
Lin Li ◽  
Qing Tao Long
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Singularity Detection ◽  
Fault Recognition ◽  
The Neural Network ◽  
Simulation Results

Using the principle of wavelet transform in the aspect of signal singularity detection analyzes and detects the electric power system fault signal. Then we extract signal feature near the fault moment and sent the feature vectors into the neural network. The simulation results fully prove the effectiveness and superiority of combining wavelet transform and neural network in electric power system fault recognition.

A Variable Step-Size BLMS Adaptive Jamming Cancellation Algorithm Based on FFT

2014 ◽  
Vol 631-632 ◽  
pp. 116-120
Author(s):  
Cun Qian Feng ◽  
Bing Qian Lu
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Adaptive Filter ◽  
High Efficiency ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Simulation Results

There are a lot of advantages for the traditional LMS algorithm, such as being simple in structure, being good in stability and so on, but this algorithm has defects that its computational complexity increases rapidly with the order of the adaptive filter. In order to apply the LMS algorithm to the adaptive jamming cancellation system, real time of the LMS algorithm must be improved. This paper deals with the signal which segmented into several areas, utilizes the FFT technology to achieve the blocked algorithm, and finally brings variable step-size method into the new algorithm. The high efficiency of the new algorithm is verified by simulation results.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

2011 ◽  
Vol 328-330 ◽  
pp. 1763-1767
Author(s):  
Jian Qiang Shen ◽  
Xuan Zou
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Radon Transform ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Line Detection ◽  
Original Image ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill.

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

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