This paper is concerned with fuzzy entropy definition used for image segmentation. The key problem associated with this method is to find the optimal parameter combination of membership function so that an image can b...
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This paper is concerned with fuzzy entropy definition used for image segmentation. The key problem associated with this method is to find the optimal parameter combination of membership function so that an image can be transformed into fuzzy domain with maximum fuzzy entropy. An improved immune clone selection algorithm (ICSA) is proposed to search the optimal parameter combination. Then, we compare the proposed ICSA with other artificial intelligence models. The experiment indicates that the proposed method is quite effective and ubiquitous.
In the analysis of predicting financial distress based on support vector regression (SVR). irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of pre...
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In the analysis of predicting financial distress based on support vector regression (SVR). irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone selection algorithm (ICSA) to optimi/e the parameters of SVR. Additionally, the proposed KSA-SVR model that can automatically determine the optimal parameters was tested on the prediction of financial distress. Then, we compared the proposed ICSA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.
A new fuzzy identification approach using support vector regression(SVR) and immune clone selection algorithm(ICSA) is presented in this *** positive definite reference function is utilized to construct a qualified Me...
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A new fuzzy identification approach using support vector regression(SVR) and immune clone selection algorithm(ICSA) is presented in this *** positive definite reference function is utilized to construct a qualified Mercer kernel for *** an improved ICSA is developed for parameters selection of SVR,in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy ***,a set of TS fuzzy rules can be extracted from the SVR *** results show that the resulting fuzzy model not only costs less fuzzy rules,but also possesses good generalization ability.
Image segmentation is a fundamental step in image processing. Otsu's threshold method is a widely used method for image segmentation. In this paper, a novel image segmentation rnethod based on chaos immuneclone s...
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ISBN:
(纸本)9783540742012
Image segmentation is a fundamental step in image processing. Otsu's threshold method is a widely used method for image segmentation. In this paper, a novel image segmentation rnethod based on chaos immune clone selection algorithm (CICSA) and Otus's threshold method is presented. By introducing the chaos optimization algorithm into the parallel and distributed search mechanism of immune clone selection algorithm, CICSA takes advantage of global and local search ability. The experimental results demonstrate that the performance of CICSA on application of image segmentation has the characteristic of stability and efficiency.
A new method of fault diagnosis by dynamic modeling is put forward to obtain the diagnosis parameters of intelligent appliance in different running stages. The model identification approach using support vector regres...
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ISBN:
(纸本)9780769537528
A new method of fault diagnosis by dynamic modeling is put forward to obtain the diagnosis parameters of intelligent appliance in different running stages. The model identification approach using support vector regression (SVR) and immune clone selection algorithm (ICSA) is presented in this paper. The relation between process status and the temperature change rate is analyzed in the paper. For appliance fault with uncertainty, the way of fuzzy inference is applied for actualizing inference engine of fault diagnosis. Experimental results prove that the fault diagnosis method for intelligent appliance is credible in the accuracy.
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