In this work we investigate how artificial neuralnetwork.(ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neuralnetwork. This strategy is applied to predict perme...
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In this work we investigate how artificial neuralnetwork.(ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neuralnetwork. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm-neuralnetwork.strategy (GA-ANN). The proposed algorithm combines the local searching ability of the gradient-based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA-ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neuralnetwork.outperforms the gradient descent-based neuralnetwork. (C) 2011 Elsevier Ltd. All rights reserved.
An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. ...
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Marine filters are widely used in various marine auxiliary equipment and power module equipment. The quality of filter directly affects the performance of the ship's power system and decides whether the ship can o...
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ISBN:
(纸本)9781450372985
Marine filters are widely used in various marine auxiliary equipment and power module equipment. The quality of filter directly affects the performance of the ship's power system and decides whether the ship can operate normally or not. Therefore, the quality detection of the filters plays a key role in the whole system. However, the structure of marine filter is very complex, the input and output of the system are inconspicuous, so it is difficult to describe the filter effectively with an accurate model. But with the development of pattern recognition and neuralnetwork.theory, the new methodologies provide a new way for fault diagnosis. In this paper, we use the non-linear mapping properties of SOM network. and improve the inadequacy of initialization of network.weights, use "probability normal distribution" to distribute the initial weights reasonably, and by balancing the difference between weights and input vectors to determine the neighborhood range. The fault is effectively diagnosed and analyzed combined with the detection of flow and pressure signals filtered by filters, and the filters with different faults in internal structure can be distinguished, so as to achieve the purpose of analyzing the fault grade and category of filters.
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