多層感知器對輸入與權值誤差的敏感度分析及倒傳遞(BP)演算法與進化策略(ES)演算法的改善

楊盛松

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多層感知器對輸入與權值誤差的敏感度分析及倒傳遞(BP)演算法與進化策略(ES)演算法的改善

Sensitivity Analysis of the Multilayer Perceptron due to the Errors of the Inputs and Weights & Improvements in BP and ES Algorithms

楊盛松 , Ph.D　　Advisor：賀嘉律　　

英文

敏感度；多層感知器；倒傳遞演算法；進化策略演算法； evolutionary strategy ； back-propagation ； multilayer perceptron ； sensitivity

Abstract │ Reference (42) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Multilayer Perceptron (MLP) is often used in some algorithms such as Back-Propagation (BP) algorithm, Evolutionary algorithm (EA), Extreme Learning Machine (ELM) algorithm, etc. Among these algorithms, BP and EA algorithms are more commonly operated in MLP structures to implement some applications than ELM is. Furthermore, the used MLP structures always affect the performances of these algorithms. Therefore, it is a substantial work to decide a feasible MLP structure (i.e., to decide number of layers and number of neurons in each layer) for each one of these algorithms. The main work of this dissertation is to analyze the adjustment of the output of the MLP due to the adjustments of the inputs and the weights between the neurons in adjacent layers (i.e., to analyze the sensitivity of the MLP due to the errors of the inputs and weights). Different MLP structure will lead to different sensitivity value. Based on the sensitivity values, it is feasible to choose a proper MLP structure for the related algorithm. In order to study the sensitivity of a MLP, we use the Central Limit Theorem (CLT) in the statistical computation of the sensitivity. Moreover, the CLT can also be extended to the sensitivity computation of the split-complex MLP (Split-CMLP); the Split-CMLP can be used in a complex signal system such as QPSK signal system, etc. Therefore, we analyze the sensitivity of both MLP and Split-CMLP in this dissertation. On the other hand, we combine the hierarchical structure and BP algorithm in this dissertation to improve the performance of the standard BP algorithm, and this new algorithm is named as HBP algorithm. Additionally, we also introduce an approach in this dissertation to decide the operation parameters of the Evolutionary Strategy (ES) algorithm-the most popular one of the Evolutionary algorithms, to improve its performance.

Reference ( 42 ) 〈TOP〉

[1] M. Stevenson, R. Winter, and B. Widrow, “Sensitivity of feedforward neural networks to weight errors,” IEEE Trans. Neural Networks, vol. 1, pp. 71-80, Mar. 1990.
連結：
[3] S. W. Piche, “The selection of weight accuracies for Madalines,” IEEE Trans. Neural Networks, vol. 6, pp. 432-445, Mar. 1995.
連結：
[4] S. Hashem, “Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions,” in Proc. IJCNN’92, vol. 1, Baltimore, MD, 1992, pp. 419-424.
連結：
[5] L. Fu and T. Chen, “Sensitivity analysis for input vector in multilayer feedforward neural networks,” in Proc. IEEE Int. Conf. Neural Networks, vol. 1, San Francisco, CA, 1993, pp. 215-218.
連結：
[6] J. M. Zurada, A. Malinowski, and S. Usui, “Perturbation method for deleting reduntant inputs of perceptron networks,” Neurocomput., vol. 14, pp. 177-193, 1997.
連結：

Times Cited (1) 〈TOP〉

王文隆(2012)。多層感知決策回授等化器之調適性活化函數分析。臺北科技大學電資碩士在職專班研究所學位論文。2012。1-76。

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