Prothesis Movements Pattern Recognition Based on Auto-Regressive Model and Wavelet Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 2156-2161 ◽  
Author(s):  
Cheng Gao ◽  
Jiao Ying Huang ◽  
Wei Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Ar Model ◽  
Wavelet Neural Networks ◽  
Time Frequency ◽  
The Neural Network ◽  
Time Frequency Localization ◽  
Auto Regressive ◽  
The Right

Wavelet neural networks (WNN) combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prothesis movements was studied in this paper. Firstly, an AR model was used to analysis the surface myoelectric signals (SMES) which recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Four types of prosthesis movements are recognized by extracting four-order AR coefficient and construct them as eigenvector into WNN, which was used to study the correlation between SMES and wristwork. This paper compares the classification accuracy of four movements such as hand open (HO), hand close (HC), forearm intorsion (FI) and forearm extorsion (FE).The experimental results show that the proposed method can classify correctly for at least 93.75% of the test data.

The Research on Automatic Liquor Blending Systems Based on Wavelet Neural Networks

2013 ◽  
Vol 765-767 ◽  
pp. 1019-1022
Author(s):  
Lian Jun Hu ◽  
Xiao Hui Zeng ◽  
Hong Song ◽  
Qian Li
Keyword(s):  
Neural Networks ◽  
Computer Aided Design ◽  
Mathematic Model ◽  
Wavelet Neural Networks ◽  
Time Frequency ◽  
The Neural Network ◽  
Computer Aided ◽  
Time Frequency Localization ◽  
Aided Design ◽  
Self Learning

The blending of liquors is a key process in the production of liquors. According to time-frequency localization characteristics of the wavelet transform and advantages of the neural network such as ability to develop, fault-tolerance, self-adaptability, self-learning, and robustness, a mathematic model based on wavelet neural networks is proposed in liquor blending processes with the help of computer-aided design technologies, which makes liquor blending technologies more scientific.

scholarly journals Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3247
Author(s):  
M. Isabel Dieste-Velasco
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Electronic Circuit ◽  
Machine Learning Techniques ◽  
The Neural Network ◽  
Learning Techniques ◽  
Short Circuits ◽  
Test Points ◽  
The Individual

In this study, machine learning techniques based on the development of a pattern–recognition neural network were used for fault diagnosis in an analog electronic circuit to detect the individual hard faults (open circuits and short circuits) that may arise in a circuit. The ability to determine faults in the circuit was analyzed through the availability of a small number of measurements in the circuit, as test points are generally not accessible for verifying the behavior of all the components of an electronic circuit. It was shown that, despite the existence of a small number of measurements in the circuit that characterize the existing faults, the network based on pattern-recognition functioned adequately for the detection and classification of the hard faults. In addition, once the neural network has been trained, it can be used to analyze the behavior of the circuit versus variations in its components, with a wider range than that used to develop the neural network, in order to analyze the ability of the ANN to predict situations different from those used to train the ANN and to extract valuable information that may explain the behavior of the circuit.

scholarly journals Diverse time-frequency distributions integrated to an art2 network for non-destructive testing

2008 ◽  
Vol 6 (01) ◽  
Author(s):  
H. Bení­tez-Pérez ◽  
L. Medina-Gómez
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Ultrasonic Pulse ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Resonance Theory ◽  
Frequency Distributions ◽  
Time Frequency ◽  
The Neural Network ◽  
Non Destructive

The Ultrasonic Pulse-Echo technique has been successfully used in a non-destructive testing of materials. To perform Ultrasonic Non-destructive Evaluation (NDE), an ultrasonic pulsed wave is transmitted into the materials using a transmitting/receiving transducer or arrays of transducers,that produces an image of ultrasonic reflectivity. The information inherent in ultrasonic signals or image are the echoes coming from flaws, grains, and boundaries of the tested material. The main goal of this evaluation is to determine the existence of defect, its size and its position; for that matter, an innovative methodology is proposed based on pattern recognition and wavelet analysis for flaws detection and localization. The pattern recognition technique used in this work is the neural network named ART2 (Adaptive Resonance Theory) trained by the information given by the time-scale information of the signals via the wavelet transform. A thorough analysis between the neural network training and the type wavelets used for the training has been developed, showing that the Symlet 6 wavelet is the optimum for our problem.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

Design of Neural Network Based on Rough Set

2012 ◽  
Vol 263-266 ◽  
pp. 3378-3381
Author(s):  
Xue Min Zhang ◽  
Zhen Dong Mu
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Sample Test ◽  
Low Efficiency ◽  
The Right ◽  
Test Module

After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the classification boundaries are not clear, convergence low efficiency and accuracy, there may even be the state does not converge, using rough set theory, the right value to modify the function to be modified, and joined the contradictions sample test module, after the use of EEG to verify reached the deletion of number of features and the purpose to improve the classification accuracy.

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

2012 ◽  
Vol 452-453 ◽  
pp. 782-788
Author(s):  
Jin Feng Wang ◽  
Li Jie Feng ◽  
Zhao Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Evaluation Model ◽  
Wavelet Neural Network ◽  
Time Frequency ◽  
Model Based ◽  
Time Frequency Localization ◽  
Mine Flooding ◽  
Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

Gray-Box Approach for Fault Detection of Dynamical Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 451-454 ◽  
Author(s):  
Han G. Park ◽  
Michail Zak
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Fault Detection ◽  
Stochastic Model ◽  
Deterministic Model ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Simulated System ◽  
Auto Regressive ◽  
Nonlinear Components

We present a fault detection method called the gray-box. The term “gray-box” refers to the approach wherein a deterministic model of system, i.e., “white box,” is used to filter the data and generate a residual, while a stochastic model, i.e., “black-box” is used to describe the residual. The residual is described by a three-tier stochastic model. An auto-regressive process, and a time-delay feed-forward neural network describe the linear and nonlinear components of the residual, respectively. The last component, the noise, is characterized by its moments. Faults are detected by monitoring the parameters of the auto-regressive model, the weights of the neural network, and the moments of noise. This method is demonstrated on a simulated system of a gas turbine with time delay feedback actuator.

scholarly journals Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

2020 ◽  
Vol 167 ◽  
pp. 2445-2457
Author(s):  
Duddela Sai Prashanth ◽  
R Vasanth Kumar Mehta ◽  
Nisha Sharma
Keyword(s):  
Neural Network ◽  
Pattern Recognition
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