Fingerprint recognition using wavelet transform and probabilistic 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.

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Discrete wavelet transform and probabilistic neural network based algorithm for classification of fault on transmission systems

2008 Annual IEEE India Conference ◽

10.1109/indcon.2008.4768827 ◽

2008 ◽

Cited By ~ 13

Author(s):

J. Upendar ◽

C. P. Gupta ◽

G. K. Singh

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Probabilistic Neural Network ◽

Discrete Wavelet ◽

Transmission Systems

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Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i1.pp92-101 ◽

2015 ◽

Vol 5 (1) ◽

pp. 92 ◽

Cited By ~ 1

Author(s):

A. B. M. Aowlad Hossain ◽

Md. Wasiur Rahman ◽

Manjurul Ahsan Riheen

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Probabilistic Neural Network ◽

Hand Movement ◽

Hand Movements ◽

Discrete Wavelet ◽

Eeg Signals ◽

Beta Band ◽

Right Hand ◽

Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features. The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

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Penentuan Notasi Gamelan Rindik Menggunakan Metode Transformasi Wavelet

Majalah Ilmiah Teknologi Elektro ◽

10.24843/mite.2018.v17i03.p03 ◽

2018 ◽

Vol 17 (3) ◽

pp. 319

Author(s):

I Gusti Made Meri Utama Yasa ◽

Linawati Linawati ◽

N Paramaita

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Wavelet Transform ◽

Probabilistic Neural Network ◽

Wavelet Function ◽

Discrete Wavelet ◽

Daubechies Wavelet ◽

Data Process ◽

Average Accuracy ◽

Accuracy Level

Abstract—This paper present about recognition of gamelan rindik pattern using wavelet transform. Wavelet transform is used to find the special characteristic of gamelan rindik, which had previously been recorded and stored in computer with format *.wav. The data was subsequently used as training and tested data, Probabilistic Neural Network (PNN) was used to recognize gamelan rindik pattern using. The training and tasted data process used four different rindics, consisting 0f 240 gamelan rindik data. Discrete Wavelet Transform (DWT) was used as the method of feature extraction, with Symlet, Haar, and Daubechies Wavelet function. Those three functions of the wavelet shows the average accuracy level for Symlet 94.58%, Haar 93.33%, and wavelet Daubechies 94.58%.

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