|| Bishop, C.M. (1995), Neural networks for pattern recognition. Clarendon Press, Oxford, UK.|
 Bounds, D.G., Lloyd, P,J. (1988), A multilayer perceptron network for the diagnosis of low back pain. Proc. Second IEEE Int'l. Conf. Neural Networks, San Diego, July 24-27, II-481-II-489.
 Castillo, P.A., Merelo, J.J., Prieto, A., Rivas, V., Romero, G. (2000), Evolving multilayer perceptrons. Neural Processing Letters 12, 115-127.
 Castillo, P.A., Merelo, J.J., Prieto, A., Rivas, V., Romero, G. (2000), G-Prop: Global optimization of multilayer perceptrons using GAs. Neurocomputing 35, 149-163.
 Chu, C.Y., The research of defect solutions for TFT-LCD G4.5 cell process in BPN application, National Central University, Executive Master of Industrial Management, 2007.
 Cybenko,G. (1989), Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2(4), 303-314.
 Dayhoff, Judith E. (1990) Neural network architectures: an introduction. Van Nostrand Reinhold, New York.
 Feng, C.X.J., Gowrisankar, A.C., Smith, A.E., Yu, Z.G.S. (2006), Practical guidelines for developing BP neural network models of measurement uncertainty data. Journal of Manufacturing System 25(4), 239-250.
 Ham, J., Kamber, M. (2003) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco, California.
 Hart, A. (1992), Using neural networks for classification tasks-some experiments on datasets and practical advice. Journal of the Operational Research Society 43(3), 215-226.
 Haykin, S. (1994), Neural networks: A Comprehensive foundation. Prentice-Hall International, Englewood Cliffs, NJ.
 Hsieh, K.L., Lu, Y.S. (2008), Model construction and parameter effect for TFT-LCD process based on yield by using ANNs and stepwise regression. Expert Systems with Application 34, 717-724.
 Khaw, J.F.C, Lim, B.S., Lim, L.E.N. (1995), Optimal design of neural network using the Taguchi method. Neurocomputing 7, 225-245.
 Kim, Y.S., Yum, B.J. (2004), Robust design of multilayer feedforward neural networks: an experimental approach. Engineering Applications of Artificial Intelligence 17, 249-263.
 Lee, K.H, Yi, J.W., Park, J.S., Park, G.J. (2003), An optimization algorithm using orthogonal arrays in discrete design space for structures. Finite Elements in Analysis and Design 40, 121-135.
 Leonard, J.A., Kramer, M.A. (1991), Radial basis function networks for classifying process faults. Control Systems Magazine 11(3), 31-38.
 Lim, D.C., Seo, D.G., Jeong, D.H. (2005), Defect Classification for Inspection of TFT-LCD Glass. Proceedings of SPIE 6051, 60510F-1-60510F-6.
 Lin, S.W., Chou, S.Y., Chen, S.C. (2007), Irregular shapes classification by back-propagation neural networks. International Journal of Advance Manufacturing Technology 34, 1164-1172.
 Lin, S.W., Tseng, T.Y., Chou, S.Y., Chen, S.C. (2008), A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks. Expert Systems with Application 34, 1491-1499.
 Lin, T.Y., Tseng, C.H. (2000), Optimum design for artificial neural networks: an example in a bicycle derailleur system. Engineering Application of Artificial Intelligence 13, 3-14.
 Maier, H.R., Dandy, G.C. (1998), The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environmental Modelling & Software 13, 193-209.
 Maren, A., Harston, C., Pap, R. (1990), Handbook of Neural Computing Applications, Academic Press, San Diego, CA.
 Montgomery, D.C. (1997), Design and Analysis of Experiments, 5th Edition, Wiley, New York.
 NeuralWare Inc. (1991). Neural Computing, NeuralWorks Professional II/Plus and NeuralWorks Explorer.
 Packianather, M.S., Drake, P.R., Rowlands, H. (2000), Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments. Quality and Reliability international 16, 461-473.
 Park, S.H. (1996), Robust Design and Analysis for Quality Engineering, Chapman & Hall, London.
 Peace, G.S. (1993), Taguchi Method: A Hands-on Approach, Addison-Wesley, Reading, MA.
 Pham, D.T., Sagiroglu, S. (2000). Neural network classification defects in veneer boards. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214(3), 255-258.
 Sexton, R. S., Alidance, B., Dorsey, R. E. (1998). Global optimization for artificial neural network: a Tabu search application. European Journal of Operational Research 106, 570-584.
 Sukthomya, W., Tannock, J. (2005), The optimization of neural network parameters using Taguchi's design of experiments approach: an application in manufacturing process modeling. Neural Computing and Application 14, 337-344.
 Taguchi, G. (1987), System of Experimental Design, Vol. 1& 2. UNIPUB/Kraus International Publications, New York.
 Tortum, A., Yayla, N., Celik, C., Gokdag, M. (2007), The investigation of model selection criteria in artificial neural networks by Taguchi method. Physica A 386, 446-468.
 Wang, T.Y., Huang, C.Y. (2008), Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array. Expert Systems with Application 34, 1630-1641.
 Yang, T., Lin, H.C., Chen, M.L. (2006), Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study. Robotics and Computer-Integrated Manufacturing 22, 322-331.
 Yang, T., Olmen, R.V. (2004), Robust design for a multilayer ceramic capacitor screen-printing process case study. Journal of Engineering Design 15(5), 447-457.
 Yuen, C.W.M., Wong, W.K., Qian, S.Q., Chan, L.K., Fung, E.H.K. (2008), A hybrid model using genetic algorithm and neural network for classifying garment defects. Expert Systems with Applications.