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Various electromagnetic (EM) structures become more complex and often have increasing degrees of design freedom. Classical optimization methods require numerous simulation trials of different parameter combinations, which leads to a low design efficiency. To address this problem, an efficient EM structure optimization algorithm which combines differential evolution (DE) with machine learning technology...
Validation of Sensors has very important effects on the consequences of structural experiments and subsequent analyzing works. This article focus on the problem that if the data collected from the sensors are valid or not. It tested the validation of an specified acceleration sensor on a truss structure by using Naive Bayesian Classifier (NBC) based on one kind of machine learning technology whose...
Least Square Support Vector Machines (in short LS-SVM) reduces the complexity of standard SVM to O(n2). Both SVM and LS-SVM are not suitable for the large scale regression problem. This paper proposes a modifies LS-SVM based on increment datasets, all samples' knowledge is accumulated and some samples is discarded effectively in the incremental learning process. The numerical experiments on benchmark...
The concept lattice, the core data structure in the formal concept analysis, has been used widely in machine learning, data mining, etc. In the formal concept analysis, researching on constructing the concept lattice efficiently is one of the important contents. This paper presents a new algorithm called IETreeCS (Concept Sets based on Intension and Extension Tree) based on the IE-Tree (Intension...
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