頁籤選單縮合
題名 | On the Capability of a New Multilayer Perceptron for Two-Class Classification Problems= |
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作者 | 江政欽; 傅心家; |
期刊 | Journal of Information Science and Engineering |
出版日期 | 19921200 |
卷期 | 8:4 1992.12[民81.12] |
頁次 | 頁567-585 |
分類號 | 310.15 |
語文 | eng |
關鍵詞 | Neural network; Multilayer perceptron; Dichotomy; |
英文摘要 | In this paper, we propose a new Quadratic Threshold activation function for multilayer perceptorn neural networks and then discuss the capability of the neural networks for two-class classification problems. By using the Quadratic Threshold activation function in each neuron, we prove that the upper bound of the number of hidden neurons requried for solving a given two-class classification problem can be reduced by one falf compared with the conventional multilayer perceptrons which use the Threshold function. To utilize various optimization techniques in designing the learning algorithm of the new multilayer perceptorn, a differentiable Quadratic Sigmoid function is also proposed to approximate the non- diffferentiable Quadratic Threshold function. Based on the Quadratic Sigmoid function, we have designed the learning algorithm of the new multilayer perceptorn neural network in a way similar to the derivations of the backpropagation learning algorithm. Some simulation results are also demonstrated to show the effectiveness of the learning algorithm. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。