Many static neural networks have been studied extensively in financial classification problems. However, dynamic time series predictive classification using neural networks with memory, such as the Gamma Memory neural network (GMNN), may prove more accurate. In this study we compare the predictive accuracy of the GMNN to the Multilayer Perceptron neural network and the statistical approaches of...

We describe a new method for recognizing humans by their gait using back propagation neural network(BPNN), BPNN algorithm is used to recognize humans by their gait patterns. Automatic gait recognition using Fourier descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, a simple background generation algorithm is introduced to subtrac...

Hydraulic turbine runner has a complex structure, and traditional location methods can't meet its requirement. This paper describes a source location of cracks in turbine blades by combining kernel independent component analysis (KICA) with wavelet neural network (WNN). The research shows that the location accuracy of WNN combined with KICA feature extraction is the best comparing with the resu...

Feature extraction and classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-tonoise ratio, motor imagery EEG signals can be difficult to classification. In this paper,...

As tobacco redrying process has characteristics of multi-interference, strong coupling, great hysteresis, nonlinear and uncertainty, it is very difficult to establish the physical model. This paper presented an innovative method with the variable-learning-rate-based back propagation neural network (BPNN) for establishing the quality prediction model of tobacco redrying process. First, character...

This paper proposes a decoupling method for a novel tactile sensor based on improved Back Propagation Neural Network (BPNN). In the numerical experiments, the number of hidden layer nodes of the BPNN is optimized and k-fold-cross-validation (k-CV) method is also applied to construct the dataset. Furthermore, information of the tactile sensor array at different scales is used to construct the BP...

In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage of using these features is the high-speed extraction, as well as being unique to each iris. Then the back propagation neural network (BPNN) is used as a classifier. In this system, the BPNN parallel algorithms and their implem...

In this paper, we propose a method of classification of image by combining wavelet transform and neural network. Our main objective in this work is to achieve an optimal approach of classification by combining wavelet transform and neural network. The proposed scheme for successful classification is combination of a wavelet domain feature extractor and back propagation neural networks (BPNN) cl...

Multispectral imaging with 19 wavelengths in the range of 405-970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for ...

In this paper, a new feature extraction operator, the grating cell operator, is applied to analyze the texture features and classify fonts of scanned document images. This operator is compared with the isotropic Gabor filter which was also employed for font classification. In order to improve the performance, a back-propagation neural network (BPNN) classifier was applied and compared with the ...