Title: Classification of the students' scores based on neural networks
Abstract:In this paper, the data of students' scores are analyzed by using the nonlinear BP neural network algorithm with a hidden layer, the probabilistic neural network algorithm and the perceptron algorithm...In this paper, the data of students' scores are analyzed by using the nonlinear BP neural network algorithm with a hidden layer, the probabilistic neural network algorithm and the perceptron algorithm. We take 3000 students' scores to be analyzed and classified. Among these scores, 121 students' scores are trained and 2879 students' scores are tested by these three neural network algorithms. By comparing these three kinds of neural network algorithms, we can get the following results: the train errors of these three neural network algorithms are all zero, but the test errors of these three neural network algorithms are different and the test error of the probabilistic neural network algorithm is less than those of the nonlinear BP neural network algorithm and the perceptron algorithm; the train time of the BP neural network algorithm is longer than those of the probabilistic neural network algorithm and the perceptron algorithm; the test time of the probabilistic neural network algorithm is longer that those of the nonlinear BP neural network algorithm and the perceptron algorithm. The correct rate of the probabilistic neural network algorithm heads to 99.06% when net.spread∈[0.00121998 89,0.1186944 765].
The correct rate of the BP neural network algorithm changes from 98.51% to 99.06%. But the correct rate of the perceptron neural network algorithm is too low and changes from 20% to 30%. Therefore by considering the correct rate and the whole time of classification, we obtain that the probabilistic neural network algorithm is more suitable for solving the classification of the students' scores.Read More
Publication Year: 2010
Publication Date: 2010-07-22
Language: en
Type: article
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Cited By Count: 1
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