Comparative study of MLP and RBF algorithms for data classification
سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 381
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شناسه ملی سند علمی:
TECCONF04_214
تاریخ نمایه سازی: 30 شهریور 1398
چکیده مقاله:
This paper compare the performance of various multilayer perceptron (MLP) and radial basis function (RBF) neural networks on classification problems using Matlab’s Neural network toolbox. We tested the studied networks on data sets such as iris dataset, cancer dataset, glass dataset, thyroid dataset and etc. Several evaluation parameters, such as number of mean square of error (MSE), number of neurons in hidden layer, training time and accuracy (ACC), were taken into account during performance comparison of the algorithms. The results show that the Levenberg-Marquardt training algorithm and Radial basis function neural network is frequently faster and achieves better accuracy than the other algorithms for moderate size problems.
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نویسندگان
Seyed Alireza Aghvami
Department of Electricty, Payame Noor University, PO BOX ۱۹۳۹۵-۳۶۹۷, Tehran, IRAN