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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...
This paper presents the results from a neural network rule extraction algorithm applied to the LED display recognition problem. We show that pruned neural networks with small number of hidden nodes and connections are able to recognize all the 10 digits from 0 to 9. Earlier work by other researchers demonstrated how symbolic fuzzy rules can be extracted from trained neural networks to solve this problem...
In this work, we consider the amplify-and-forward (AF) two-way relay network where the two terminals TA, TB exchange their information through a relay node TR in a bi-directional manner and study the impact of the training-based channel estimation upon individual and sum-rate of the two users. We assume that in the multiple-access (MA) phase TA, TB initiate transmission by sending their training symbols...
This paper describes dynamics analysis of a small training ship and a possibility of ship pitching stabilization by adjusting engine speed. First, statistical analysis through multi-variate auto regressive(MAR) model is carried out. After upgrading the navigational system of an actual small training ship, in order to identify the model of the ship, the real data collected by sea trials on the ship...
This paper reviews applications of neural networks (NNs) in the domain of 2-D simulation soccer. We divide these into the employment of NNs for the training of low-level and high-level skills as well as coaching clients involved with high-level strategies. We conclude that the use of NNs has yielded success in these areas, but their future use may be limited to building a foundation of skills which...
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...
A detailed design and implementation of a Chinese Web-page classification system is described in this paper, and some methods on Chinese Web-page preprocessing and feature preparation are proposed. Experimental results on a Chinese Web-page dataset show that methods we designed can improve the performance from 75.82% to 81.88%.
In this paper we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of an accelerometer and we consider six levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 db to 9.59 db. We classify the...
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