In this paper, a new bp neural network classifier was constructed and optimized by Genetic Algorithm, first, the bpneuralnetwork was improved by using genetic algorithm[2] to train the initial weights values of the ...
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ISBN:
(纸本)9783642316555
In this paper, a new bp neural network classifier was constructed and optimized by Genetic Algorithm, first, the bpneuralnetwork was improved by using genetic algorithm[2] to train the initial weights values of the bpneuralnetwork[3], second, a new classifier was constructed based on the new bpneuralnetwork optimized by Genetic Algorithm. Finally, data simulation experiment was taken and the result of data simulation with famous IRIS data shows that the new bp neural network classifier improved by the Genetic Algorithm has higher accuracy of classification and greater gradient of convergence than the bp neural network classifier which Proposed in literature [3].
In order to reduce passengers' waiting time, improve the quality of security inspection, minimize the cost of service resource and maximize the utilization of security resource, the index system of risk-based airp...
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ISBN:
(纸本)9789881563903
In order to reduce passengers' waiting time, improve the quality of security inspection, minimize the cost of service resource and maximize the utilization of security resource, the index system of risk-based airport passenger classification was constructed to identify passenger risk levels. Firstly, the index system of risk-based airport passenger classification was initially constructed through literature review and expert consultation. Secondly, the index system of risk-based airport passenger classification is optimized with the help of relevant professionals' questionnaires and SPSS22.0 software. Finally, bp neural network classifier is selected and which was optimized by PSO algorithm. The classifier was trained in simulations and tested for classification effects by using the sample data of questionnaire survey. Test results show that the index system of risk-based airport passenger classification can be obtained to measure passenger risk degree from five aspects, natural condition, occupation status, economic condition, credit situation and flight condition. The effectiveness of the index system and the bp neural network classifier were verified by the classification results of 5 passengers through the classifier.
In order to reduce passengers’ waiting time, improve the quality of security inspection, minimize the cost of service resource and maximize the utilization of security resource, the index system of risk-based airport...
详细信息
In order to reduce passengers’ waiting time, improve the quality of security inspection, minimize the cost of service resource and maximize the utilization of security resource, the index system of risk-based airport passenger classification was constructed to identify passenger risk levels. Firstly, the index system of risk-based airport passenger classification was initially constructed through literature review and expert consultation. Secondly, the index system of risk-based airport passenger classification is optimized with the help of relevant professionals’ questionnaires and SPSS22.0 software. Finally, bp neural network classifier is selected and which was optimized by PSO algorithm. The classifier was trained in simulations and tested for classification effects by using the sample data of questionnaire survey. Test results show that the index system of risk-based airport passenger classification can be obtained to measure passenger risk degree from five aspects, natural condition, occupation status, economic condition, credit situation and flight condition. The effectiveness of the index system and the bp neural network classifier were verified by the classification results of 5 passengers through the classifier.
It's very common to use the skin texture of gray level co-occurrence matrix to calculate the four most representative eigenvalues of human facial skin image: energy, moment of inertia, correlation and entropy. To ...
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ISBN:
(纸本)9781510817388
It's very common to use the skin texture of gray level co-occurrence matrix to calculate the four most representative eigenvalues of human facial skin image: energy, moment of inertia, correlation and entropy. To test whether the four eigenvalues can represent the skin texture information, the article designed a verification experiment: the article used comparison data included arithmetic average roughness(Ra), average roughness(Rz), and smooth depth data(Rt) measured from DERMATOP V3 of CK in Germany, and experimental data included the four eigenvalues, to do principal component analysis, respectively, for unrelated principal component as the input data of bp neural network classifier. The experimental results show that using the four eigenvalues, the classification accuracy is higher. The method using gray level co-occurrence matrix to extract facial skin texture eigenvalue can relatively reflect the degree of human facial texture state than texture information measured by DERMATOP V3, which provides a simple and effective method for the data acquisition of skin texture.
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