In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement). According to the motor imagery EEG data...

As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simula...

An “electronic nose” has been used for the detection of adulterations of sesame oil. The system, comprising 10 metal oxide semiconductor ensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, eature extraction techniques were employed to choose a set of optimal discriminant variables. Principal compo...

The aim of this paper is to develop the design of classifier using Artificial Neural Network for patients survival analysis based on echocardiography dataset. Survival analysis can be considered a classification problem in which the application of machine learning methods is appropriate. Survival analysis plays an important role not only for health care policy markers, but also for the clinicia...

This paper proposes a novel ensemble learning approach based on logistic regression (LR) and artificial intelligence tool, i.e. support vector machine (SVM) and back-propagation neural networks (BPNN), for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM and BPNN are introduced. Then, the forecasting results by LR are introduce...

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designe...

Automatic modulation recognition which is one of the key technologies in no-cooperative communications has extensive application prospects in civilian and military fields. The design of classifier played a decisive role in recognition results. The classifier based on back propagation (BP) neural network is better in the existing methods. However, the traditional back propagation neural network ...

We examined the effects of changing learning parameters on the learning procedure and performance of back-propagation neural networks used to pick seismic arrivals. The results show that such change mainly affects the speed of convergence of the learning procedures, and does not affect the BPNN structure and its overall performance. A relationship between the learning parameters and iteration n...

Sleep stage influence autonomic nervous system, this influence can be investigated by analysis of ECG signal. This paper presents system aimed to score sleep-wake stages using only the electrocardiogram (ECG) records. The feature extraction stage described in this paper was performed using methods of Heart Rate Variability analysis (HRV) and Detrended fluctuation analysis (DFA). These features ...

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to ...