نتایج جستجو برای: the combination of artificial neural network and wavelet transform wavelet
تعداد نتایج: 25089633 فیلتر نتایج به سال:
in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...
Automatic human iris verification is an active research area with numerous applications in security purposes. Unfortunately, most of feature extraction methods in human iris verification systems are sensitive to noise, scale and rotation. This paper proposes an integrated hybrid model among Discrete Wavelet Transform, Wavelet Neural Network and Genetic Algorithms for optimizing the feature extr...
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. this paper proposes a three module system for classification of electrocardiogram (ecg) beats. these modules are: denoising module, feature extraction module and a classification module. in the first module the stationary wavelet transform (swf) is used for noise reduction of ...
In this paper, we present two feature extraction techniques for the classification of ultrasonic NDE signals acquired from weld inspection regions of boiling water reactor piping of nuclear power plants. The classification system consists of a pre-processing block that extracts features from the incoming patterns, and of an artificial neural network that assigns the computed features to a parti...
Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples...
Iris recognition is one of the challenging problems inhuman computer interaction. An automated iris recognition system requires an efficient method for classification of iris region in the face sequence, extraction of iris features, and construction of classification model. In recent years, Neural Networks (NN) has demonstrated excellent performance in a variety of classification problems. In t...
In this paper, a single-ended fault location method is presented based on a circuit breaker operation using the frequencies of traveling waves. The proposed method receives the required data from voltage traveling waves with the aid of Fast Fourier Transform (FFT) and Wavelet Transform. Then, the Artificial Neural Network (ANN) identifies fault type and determines its location. In order to eval...
This paper proposes an approach for the protection of transmission lines with FACTS based on Artificial Neural Networks (ANN) using Wavelet Transform (WT). The required features for the proposed algorithm are extracted from the measured transient current and voltage waveforms using discrete wavelet transform (DWT). Those features are employed for fault detection and faulted phase selection usin...
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