The use of wavelet transform as a preprocessor for the neural network detection of EEG spikes

2002 ◽  
Author(s):  
T. Kalayci ◽  
O. Ozdamar ◽  
N. Erdol
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
The Neural Network

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.

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.

scholarly journals PENERAPAN TRANSFORMASI DATA DISCRETE WAVELET TRANSFORM PADA NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM

2019 ◽  
Vol 2 (1) ◽  
pp. 17-22
Author(s):  
Indah Suryani
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Stock Prices ◽  
Data Transformation ◽  
Discrete Wavelet ◽  
Data Set ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method

Research on stock prices is still interesting for researchers. As in this study, ANTM's stock price closing data is used as a data set that is processed to be then predicted in the future. The Neural Network method is a method that is very widely used by researchers because of its various advantages. While the Discrete Wavelet Transform method is used to transform data to improve data quality so that it is expected to improve Neural Network performance. Based on experiments conducted by the Neural Network method with the Binary Sigmoid activation function which also carried out data transformation with Discrete Wavelet Transform, it has produced a smaller RMSE than prediction experiments without using data transformation with Discrete Wavelet Transform.   Keywords: Prediction, Stock Prices, Neural Network, Discrete Wavelet Transform

Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network

2016 ◽  
Vol 13 (10) ◽  
pp. 7074-7079
Author(s):  
Yajun Xu ◽  
Fengmei Liang ◽  
Gang Zhang ◽  
Huifang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Model ◽  
Neural Network Model ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Human Face ◽  
Gabor Wavelet Transform ◽  
Gabor Function ◽  
The Neural Network

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

Research on Fault Detection Method of Converters

2012 ◽  
Vol 229-231 ◽  
pp. 1150-1153
Author(s):  
Wen Zhong Ma ◽  
Ke Cheng Chen ◽  
Yang Shan ◽  
Yan Li Wang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Detection Method ◽  
Fault Tolerant ◽  
Experimental Results ◽  
The Neural Network ◽  
Operation Control ◽  
Fault Information

The influence of converter faults to the system is introduced and the fault detection method based on wavelet transform and neural network is proposed in this paper. The fault information can be decomposed by wavelet transform, then the fault eigenvectors can be extracted and put into neural network for training and testing. Finally the neural network outputs specific codes, and thus the fault location and fault components of converters are confirmed, which lays the foundation for the fault-tolerant operation control of converters. Simulation and experimental results show the correctness and effectiveness of the method.

Critical Knock Diagnosis for Gasoline Engines Based on Neural Network with Wavelet Transform and Fuzzy Clustering

2012 ◽  
Vol 455-456 ◽  
pp. 1084-1089
Author(s):  
Jian Guo Yang ◽  
Yan Yan Wang ◽  
Bo Lin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Gasoline Engine ◽  
Engine Performance ◽  
Discrete Wavelet ◽  
New Approach ◽  
Gasoline Engines ◽  
Input And Output ◽  
The Neural Network ◽  
The Common

. It is difficult to detect critical knock for a gasoline engine by the common method of knock diagnosis. In this paper, a new approach is presented to detect critical knock for gasoline engines. Based on this approach knock diagnosis consists of four steps. Firstly, discrete wavelet transform (DWT) is chosen as a pre-processor for a neural network to extract knock characteristic signals; Secondly, four characteristic factors are selected and calculated from knock characteristic signals; Thirdly, degree of memberships of the characteristic factors are calculated as the input and output of the neural network; and finally a RBF(Radial Basis Function) neural network is chosen, trained and applied to detect critical knock. Knock experiments were performed on a gasoline engine, and the application of the presented approach was studied. The results show that the presented method is practicable and can be applied to control the ignition of a gasoline engine working under critical knock which is admitted as an improved state of engine performance.

scholarly journals Arm Movements Effects in Response to Posture Instability

2021 ◽  
Author(s):  
Sara Manifar
Keyword(s):  
Neural Network ◽  
Quality Of Life ◽  
Wavelet Transform ◽  
Social Impact ◽  
Spinal Cord Injuries ◽  
Training Programs ◽  
The United States ◽  
Region Detection ◽  
The Neural Network

In recent years, because of an increasing aging population there are higher incidences of falling according to epidemiological reports. Because of this high frequency the prevention of falls becomes a major concern. Evidence of the high occurrence and significant cost of falls on health-related quality of life, significant financial load on the health care system, and on their social impact has been provided by various epidemiological studies. Falls are the second leading cause of traumatic brain injury (TBI), which is a major cause of death in many countries, especially the United States. Balance impairments are frequent and particularly high among people who suffer from stroke, TBI, incomplete spinal cord injuries, Parkinson’s disease, multiple sclerosis and diabetic peripheral neuropathy, and in general for people who suffer from different neurological disorders. For all of these groups, balance disorders have a major social and quality of life implications, which require attention and exploration of effective ways to evaluate risk and develop training programs that prevent falls. According to the literature, the most important factors for fall prevention are suitable training programs and the availability of feasible and cost-effective comprehensive risk measurement [1, 2]. This thesis describes the acquisition of acceleration data of a human body while maintaining balance on a balance board with three-axis accelerometers. Three different algorithms of balance region detection, the wavelet transform, and the neural network were developed to segment and classify the unstable regions of the accelerometer signal. To simplify the calculation of these algorithms vector processing technique was used. The experimental results show that arms have an effective role in the improvement of balance. From the balance region detection the duration and amount of activity can be found which will be good for prediction of falls. The wavelet transform is the best way to separate unstable periods from one another. For classification of stable and unstable parts of movements, the neural network is the best technique. It is effective to compare the amount of stable and unstable parts in more detail. The results suggest the specific role of the dominant and non-dominant arms.

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