Data cube computation attracts more and more attentions of researchers, however, this method can't find the interest information efficiently. An algorithm of mining all the exceptional cells in data cube is propos...
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Hierarchical and k-means clustering are two major analytical tools for unsupervised microarray datasets. However, both have their innate disadvantages. Hierarchical clustering cannot represent distinct clusters with s...
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A neural network consists of units, arranged in layers, which convert an input vector into some output. Each unit takes an input, applies a function to it and then passes the output on to the next layer. Generally the...
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Short-Term load forecasting is one of the important tasks of the power industry. Many relative information impacts prediction results. In this paper, an improved differential evolution algorithm and a new definition f...
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The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors o...
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In order to prevent premature convergence of constriction factor particle swarm optimization (cfPSO), an improved version of cfPSO is presented in this paper. Firstly, a new standard of premature convergence was set u...
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In order to prevent premature convergence of constriction factor particle swarm optimization (cfPSO), an improved version of cfPSO is presented in this paper. Firstly, a new standard of premature convergence was set up to determine the algorithm relapsing into local extremum. Then an improved re-initialization method was design to insure the uniform distribution of swarm in search space. In this method, the search space was divided into S, the size of swarm, sub-spaces. Each particle was reinitialized in respectively sub-space. At the same time, all particles were compelled to clear its memory. Simulation results show that the performance was improved significantly.
A series of support vector machine (SVM) forecast experiments are carried out to reveal the relation between the SVM training sample size and SVM correct forecast ratio for simulation experiment results. Experiment re...
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Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatic...
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ISBN:
(纸本)9781424458721;9781424458745
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by discrete activation values of hidden units. According to discrete activation values of this hidden unit, cluster weights from input units to it. The incremental rules are extracted and the existing rule set is updated based on this algorithm. The result shows this method is quite valuable.
A key security level gradation method is proposed which is helpful to counter IEA (Iterative Encryption Attack) and provides RSA with favorable immunity against it. Firstly, we analyze the course of IEA in detail, and...
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A key security level gradation method is proposed which is helpful to counter IEA (Iterative Encryption Attack) and provides RSA with favorable immunity against it. Firstly, we analyze the course of IEA in detail, and then present the concept of security grade of key-pair and the hierarchy of grades, and propose an algorithm that grades security grade of key-pairs. Secondly, we define the concept of attack cost, and then launch a series of experiments for the purpose of exploring the relationship between attack cost and key security grade. At last, it is demonstrated that if to choose keys properly by this method RSA is improved to be effective to counter IEA in aspect of computation power.
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