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A feature selection approach combining genetic algorithm (GA) with mutual information (FSGM) is proposed. In fact, FSGM is a genetic algorithm applied to feature selection. For feature selection task, an individual of GA represents a feature subset, and the fitness function is the evaluation of the feature subset. With elaborating design, the global searching and completely evaluation can be realized...
According to the complexity of the semiconductor manufacturing processes, a RBF neural network improved by GA is put forward to apply in the semiconductor production modeling and predict the line performance indexes. The improved RBF algorithm can play better role in dealing with the dynamic real-time data from the line, building predictive models to describe line dynamic behavior, and presenting...
The effect of the shock environment induced by clamp band satellite-rocket separation is usually analyzed by the shock response spectrum (SRS). This paper presents a method of predicting SRS data in a new shock environment on the basis of great accumulated shock tests. Firstly, the shock response spectrums that correspond to the time domain signals of the existing shock tests are calculated by means...
Currently localization algorithms for mobile sensor networks are mostly based on Sequential Monte Carlo method. However they appear either low sampling efficiency or demand high beacon density requirement issues to achieve high localization accuracy. Aiming to solve the problems, we proposed an improved algorithm called Genetic and Weighting Monte Carlo Localization (GWMCL) in which we apply the Genetic...
Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
Mesh denoising is an essential process in many geometric applications. We describe a simple and efficient mesh denoising approach based on genetic algorithm. The raw mesh is smoothed using a floating-point genetic algorithm that is more flexible than the usual binary genetic algorithms, and can handle non-smooth regions containing several local extrema. The fitness function selected is a weighted...
This paper uses genetic algorithm to optimize the relevance vector machine algorithm to extract the characteristic vector of fault classification, and by contrasting with relevance vector machine, the support vector machine and BP neural network method, it is know that the relevance vector machine optimized by genetic algorithm (ga) can more accurately classify the fault type of conclusion.
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