[ 1] L.Li, D.K.Y. Low and M.Ghoreshi, “Hole Taper Characterisation and Control in Laser Percussion Drilling”, Laser Processing Research Centre, UMIST, Manchester, UK
[ 2] Yilbas, B.-S., 1987, “Study of Affecting Parameters in Laser Hole Drilling of Sheet Metals”, Trans. ASME: J. Eng. Mat.&Tech., 109: 282-287
[ 3] Hamoudi, W.-K., Rasheed, B.-G., 1995, “Parameters Affecting Nd:YAG Laser Drilling of Metals”, International Journal for the Joining of Materials, 7:63-69
[ 4] Yilbas, B.-S. and Yilbas, Z., “Parameters affecting hole geometry in laser drilling of Nimonic 75”, Lasers in Motion for Industrial Applications, Proceedings of the SPIE, Vol. 744, 1987, pp. 87-91
[ 5] K. Hornik, “Multilayer Feedforward Networks are Universal
Approximators”, Neural Networks, Vol. 2, pp. 359-366, 1989
[ 6] U. Bhattacharya, “Self-adaptive Learning Rates in Backpropagation Algorithm Improve Its Function Approximation Performance”, 1995. Proceedings., IEEE International Conference on Neural Networks, Vol. 5, pp. 2784-2788, 1995
[ 7] Yo-Ping Huang, “Improving the Backpropagation Learning Speed with Adpative Neuro-Fuzzy Technique”, Proceeding of 1993
International Joint Conference on Neural Networks, Vol 3,
pp. 2897-2900, 1993
[ 8] A. Zaghw, “An Automated Approach for Selecting the Learning Rate and Momentum in Back-Propagation Networks”, IEEE International Conference on Neural Networks, Vol 1, pp. 464-469, 1994
[ 9] M.E. Rimer, “Speed Training: Improving the Rate of
Backpropagation Learning through Stochastic Sample Presentation”, IEEE 2001
[10] D. Nguyen, “Improving the Learning Speed of 2-Layer Neural
Networks by Choosing Initial Values of the Adaptive Weights”,
IJCNN International Joint Conference on Neural
Networks,vol.3 ,pp.21-26,1990
[11] Yolanda M. Pirez and Dilip Sarkar, ”Back-Propagation Algorithm with Controlled Oscillation of Weights”, Neural Networks, IEEE International Conference 1993, vol.1, P21-26
[12] Hagan,“Neural Network Design”, PWS PUBLISHING COMPANY
1995
[13] S.Haykin, “Neural Networks : A Comprehensive Foundation”,
Prentice Hall 1999
[14] N. Sundararajan, “Parallel Architectures for Artificial Neural Networks”, IEEE Computer Society, 1998
[15] C. Principe, “Neural and Adaptive Systems”, John Wiley & Sons Inc. 1999
[16] F.Scradelli, A.H. Tsoi, “Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results”, Neural Networks, 1998
[17] M.Riedmiller, “A Director Adaptive Method for Faster
Backpropagation Learning: The RPROP Algorithm”, IEEE 1993
[18] L. Marquez, “Function Approximation Using Backpropagation and General Regression Neural Networks”, IEEE 1993
[19] David S. Chen, “A Robust Back Propagation Learning Algorithm for Function Approximation”, IEEE 1994
[20] Martin T. Hagan, “Training Feedforward Networks with the
Marquart Algorithm”, IEEE 1994
[21] J. Wang, “On the Design Principles of the Functional Link Nets”, IEEE 1990
[22] W.J. Kim, “Application of Neural Networks to Signal Prediction in Nuclear Power Plant”, IEEE 1993
[23] J. Lee, “Improvement on Function Approximation Capability of Backpropagation Neural Networks”, 1991 IEEE International Joint Conference on Neural Networks, vol.2, pp.1367-1372,1991
[24] 謝德宇,「雷射切雕機的精度調校自動化」,國立交通大學機械工程研究所, 2002
[25] 黃偉治,「以類神經網路控制可變阻尼及勁度結構之位移研究」,
國立交通大學土木工程研究所, 2002
[26] 謝冠華,「類神經網路應用於機台設定及設備選擇: 以塑模機為
例」,國立清華大學工業工程研究所, 2000
[27] 羅華強,類神經網路-MATLAB的應用,清蔚科技, 2001
[28] 葉怡成,類神經網路模式應用與實作,儒林圖書, 1997
[29] 蔡宗河,CO2雷射加工,全華科技, 1995
[30] 林三寶,雷射原理與應用,全華科技, 1987