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Based on the annual report data between 1995 and 2005 of all listed companies (LCs), the 25 initial financial indexes, widely used by experts and researchers aboard and at home, was deduced to 14 effective evaluation indicators using factor analysis and principal component analysis (PCA). The 14 evaluation indicators, covering five aspects for comprehensive evaluation of competitiveness of LC, retain...
In this research, the combination of modal data is used to identify the damage of a FEM model using neural networks. The identification ability with different levels of noise and incomplete mode shapes are also investigated. It has been proved that the neural network using combination of modal parameters as input has a excellent identification ability with ideal error tolerance and robustness. Numberical...
Focusing on the problem in production practice of sintering process, a novel classifier based on BP learning algorithm is proposed for on-line quality inference of sintered ore. In order to speed up the convergence rate of BP learning algorithm, the learning algorithm with adaptive variable step-size is adopted. On the basis of the above work a quality prediction model is proposed in this paper. Experimental...
Fault diagnosis of induction motor is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. Due to environmental stress and many others reasons different faults occur in induction motor. Many researchers proposed different techniques for fault detection and diagnosis. However, many techniques available presently...
This paper presents the classification of benign and malignant breast tumor based on fine needle aspiration cytology (FNAC) and probabilistic neural network (PNN). Five hundred and sixty nine sets of cell nuclei characteristics obtained by applying image analysis techniques to microscopic slides of FNAC samples of breast biopsy have been used in this study. These data were obtained from the University...
This paper presents a new approach to image restoration based on ANN, considering the learning of the inverse process using a standard image for training under a multiscale approach. Different models of ANN were tested and compared with the traditional techniques. The standard image was artificially degraded to simulate some types of frequent degradation problems. Due to the huge amount of data generated...
The posterior union decision-based neural network (PUD-BNN) has been proposed in our previous work for dealing with face recognition task subject to partial occlusion and distortion. However, one difficult of this method is inaccurate to model classes with only a single, or a small number of training samples. In this paper, we proposed an extern approach to tackle above problem by two strategies....
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