Adaptive Neural Networks Control of Drug Dosage Regimens in Cancer Chemotherapy

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2003, Pages 154-159

  Floares, Alexandra ^a   Floares, Carmen ^a   Cucu, Mihai ^a   Lazar, Lucian ^a  

^a THE ONCOLOGY INSTITUTE PROF DR ION CHIRICUTA   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DRUG DOSAGE; NEURAL NETWORKS; OPTIMAL CONTROL SYSTEMS; TUMORS;

OPTIMAL SOLUTIONS;

CHEMOTHERAPY;

EID: 0141573480     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. R. B. Martin, "Optimal control drug scheduling of cancer chemotherapy", Automatica, vol. 28, pp. 1113-1123, 1992.
2. B. Bojkov, R. Hansel, and R. Luus, "Application of direct search optimization to optimal control problems", Hung J. Ind. Chem., vol. 21, pp. 177-185, 1993.
3. R. Luus, T. H. Jaakola, "Optimization by direct search and systematic reduction of the size of search region", AlChE J., vol. 19, pp. 760-766, 1973.
4. R. Luus, F . Karting, F. J. Keil, "Optimal drug scheduling of cancer chemotherapy by direct search optimization", Hung J. Ind. Chem., vol. 23, pp. 55-58, 1995.
5. K. C. Tan, E. F. Khor, J. Cai, C. M. Heng, and T. H. Lee, "Automating the drug scheduling of cancer chemotherapy via evolutionary computation", Art. Intel. Med., vol. 25, pp. 169-185, 2002.
6. V. T. DeVita Jr., S. Hellman, S. A. Rosenberg, CANCER. Principles and Practice of Oncology, 6th ed, Philadelphia: JB Lippincott,2001.
7. W. M. Hryniuk, "The importance of dose intensity in the outcome of chemotherapy", in Important advances in oncology, V. T. DeVita, S. Hellman, and S. A, Rosenberg, Eds. Philadelphia: JB Lippincott, 1988:121
8. E. F. Carrassco and J. R. Banga, "Dynamic optimization of batch reactors using adaptive stochastic algorithms", Ind. Eng. Chem. Res., vol. 36, pp. 2252-2261, 1997.
9. R. Luus, "Comments on dynamic optimization of batch reactors using adaptive stochastic algorithms", Ind. Eng. Chem. Res., vol. 37, pp. 305-311.
10. th National Congres of Neuroendocrinology, p. 20, 2000.
11. M. T. Hagan, and H. B. Demuth, "Neural Networks for Control", Proceedings of the 1999 American Control Conference, San Diego, CA, pp. 1642-1656, 1999.
12. ndd edition, 1999.
13. M. Norgaard, "Neural Network Based System Identification Toolbox", Tech. Report. 00-E-391, Department of Automation, Technical University of Denmark, 2000.
14. X. He and H. Asada, "A New Method for Identifying Orders of Input-Output Models for Nonlinear Dynamic Systems", Proc. of the American Control Conf., S.F., California, 1993.
15. K. S Narendra, and S. Mukhopadhyay, "Adaptive Control Using Neural Networks and Approximate Models", IEEE Transactions on Neural Networks, Vol. 8, 1997, pp. 475-485.
16. M. T. Hagan, O. De Jesus, and R. Schultz, "Training Recurrent Networks for Filtering and Control," Chapter 12, in Recurrent Neural Networks: Design and Applications, L. Medsker and L.C. Jain, Eds., CRC Press, 1999, pp. 311-340.
17. M. Y Chang, Optimal Scheduling of Cancer Chemotherapy, B. E. Thesis, Singapore, Department of Electrical and Computer Engineering, National University of Singapore, 2000.