Aiming at the problem of anomaly detection time series data with unbalanced distribution among classes, a method based on depth convolution neural network is proposed. With increasing trading scale futures market, it covers more and economic financial fields, volatility market intense, which constantly presents many complex phenomena that cannot be explained by other classical theories. Investo...

Statistical analysis is used to analyze seven temporal series obtained from respiratory flow signals of 66 patients on weaning trials. In which, 33 patients belong to successful group (SG), and 33 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. Patients were then classified with a pattern recognition neural network, obtaining 78.78 % of accurac...

Neural networks are widely used as classifiers in many pattern recognition problems because of good generalization abilities, what is a crucial issue in any practical application. However, vast majority of neural network architectures demands a huge computational effort for the training process, what in turn limits such solutions from application in one important domain of pattern recognition, ...

Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power, and generalization ability. The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering...

INTRODUCTION "Change-glasses" approach in pattern recognition [1] relies on the assumption that there are subspaces of the initial feature space where the chosen classification rule can be replaced by another, more competent, one (see, e.g. [2]). This will hopefully lead to a better classification accuracy in comparison with that obtained through one rule only. This classification strategy bear...

The detection of structural damage from the high-frequency local impedance spectra is addressed with a spectral classification approach consisting of features extraction followed by probabilistic neural network pattern recognition. The paper starts with a review of the neural network principles, followed by a presentation of the state of the art in the use of pattern recognition methods for dam...

In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...

Since the conventional feedforward neural networks for character recognition have been designed to classify a large number of classes with one large network structure, inevitably it poses the very complex problem of determining the optimal decision boundaries for all the classes involved in a high-dimensional feature space. Limitations also exist in several aspects of the training and recogniti...

In the modern sawmill industry automatic grading of the products is one of the key issues in increasing the production quality. The surface defects that determine the grading are identiied according to the physiological origin of the defect, such as dry, encased or decayed knot. Variations within the classes are large since the knots can have diierent shapes, sizes and color, and each class has...