[1] | Watanabe, K., Matsuura, I., Abe, M., Kubota, M. and Himmelblau, D. M., “Incipient fault diagnosis of chemical processes via artificial neural networks,” AIChE Journal, 35 (11), 1803-1812, 1989. |
|
[2] | Koivo, H. N., “Artificial neural networks in fault diagnosis and control,” Control Engineering Practice, 2 (1), 89-101, 1994. |
|
[3] | Sharma, R., Singh, K., Singhal, D. and Ghosh, R., “Neural network applications for detecting process faults in packed towers,” Chemical Engineering and Processing: Process Intensification, 43 (7), 841-847, 2004. |
|
[4] | Behbahani, R. M., Jazayeri-Rad, H. and Hajmirzaee, S, “Fault detection and diagnosis in a sour gas absorption column using neural networks,” Chemical engineering & technology, 32 (5), 840-845, 2009. |
|
[5] | Eslamloueyan, R., “Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process,” Applied Soft Computing, 11 (1), 1407-1415, 2011. |
|
[6] | Vapnik, V., The nature of statistical learning theory. New York, Springer, 1999. |
|
[7] | Widodo, A. and Yang, B.S. “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, 21 (6), 2560-2574, 2007. |
|
[8] | Tyagi, C. S., “A comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing,” Neuron, 1, 309-317, 2008. |
|
[9] | Lee, M. C. and To, C., “Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress,” International Journal of Artificial Intelligence & Applications, 1(3), 31-43, 2010. |
|
[10] | Azar, A. and El-Said, S., “Performance analysis of support vector machines classifiers in breast cancer mammography recognition,” Neural Computing and Applications, 1-15, 2013. |
|
[11] | Chiang, L. H., Kotanchek, M.E. and Kordon, A. K., “Fault diagnosis based on Fisher discriminant analysis and support vector machines,” Computers & chemical engineering, 28 (8), 1389-1401, 2004. |
|
[12] | Yélamos, I., Graells, M., Puigjaner, L. and Escudero, G., “Simultaneous fault diagnosis in chemical plants using a multilabel approach,” AIChE Journal, 53 (11), 2871-2884, 2007. |
|
[13] | Mao, Y., Xia, Z., Yin, Z., Sun, Y. and Wan, Z., “Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature selection,” Chinese Journal of Chemical Engineering, 15(2), 233-239, 2007. |
|
[14] | Yélamos, I., Escudero, G., Graells, M. and Puigjaner, L., “Performance assessment of a novel fault diagnosis system based on support vector machines,” Computers & Chemical Engineering, 33 (1), 244-255, 2009. |
|
[15] | Abe, S., Support vector machines for pattern classification. New York, Springer, 2005. |
|
[16] | Hsu, C. W. and Lin, C. J., “A comparison of methods for multiclass support vector machines,” Neural Networks, IEEE Transactions on, 13(2), 415-425, 2002. |
|
[17] | Whitley, D., “A genetic algorithm tutorial,” Statistics and computing, 4(2), 65-85, 1994. |
|
[18] | Sorsa, T., Koivo, H. N. and Koivisto, H., “Neural networks in process fault diagnosis,” Systems, Man and Cybernetics, IEEE Transactions on, 21 (4), 815-825, 1991. |
|
[19] | Hsu, C. W., Chang, C.C. and Lin, C. J., “A practical guide to support vector classification,” 2010. |
|
[20] | Chang, C.C. and Lin, C. J., “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 2011. |
|