Abstract
Modeling and optimization are two dynamic fields of studying interest for engineers and researchers in a variety of disciplines from science to engineering. Modeling is a process in which a process or phenomenon is predicted with adoption of the trend or a code of response from the system that is under investigation. When data on the problem are available, it is possible to extract a model (mathematical, statistical, numerical, etc.) based on which the prediction in a similar condition or a defined situation is predictable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Haykin, S. S. (1999). Neural networks: A comprehensive foundation. Upper Saddle River, NJ, USA: Prentice-Hall.
Jaiswal, S., Benson, E. R., Bernard, J. C., & Van Wicklen, G. L. (2005). Neural network modelling and sensitivity analysis of a mechanical poultry catching system. Biosystems Engineering, 92(1), 59–68.
Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans on Systems, Man and Cybernetics, 23(3), 665–685.
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neurofuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NY: Prentice-Hall.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. Transactions on Systems, Man, and Cybernetics 15, 116–132.
Petković, D., Gocic, M., Trajkovic, S., Shamshirband, S., Motamedi, S., Hashim, R., & Bonakdari, H. (2015). Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Computers and Electronics in Agriculture, 114, 277–284.
Karaağaç, B., İnal, M., & Deniz, V. (2012). Predicting optimum cure time of rubber compounds by means of ANFIS. Materials and Design, 35, 833–838.
Vapnik, V. (1995). The nature of statistical learning theory (2nd ed.). New York, NY: Springer. 309Â pp.
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press. 626 pp.
Vapnik, V. N. (2000). The nature of statistical learning theory. New York: Springer.
Gunn, S. R. (1998). Support vector machines for classification and regression. Technical report. UK: Department of Electronics and Computer Science, University of Southampton.
Petković, D., Shamshirband, S., Saboohi, H., Ang, T. F., Anuar, N. B., & Pavlović, N. D. (2014). Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper. Applied Intelligence, 41(3), 887–896.
Fleetwood, K. (2004, November). An introduction to differential evolution. In Proceedings of Mathematics and Statistics of Complex Systems (MASCOS) One Day Symposium, 26th November. Brisbane, Australia.
Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist Competitive algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE congress on evolutionary computation (pp. 4661–4667).
Xing, B., & Gao, W. J. (2014). Imperialist competitive algorithm. In: Innovative computational intelligence: A rough guide to 134 clever algorithms (pp. 203–209). Berlin: Springer International Publishing.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Taghavifar, H., Mardani, A. (2017). Application of Artificial Intelligence on Modeling and Optimization. In: Off-road Vehicle Dynamics. Studies in Systems, Decision and Control, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-319-42520-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-42520-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42519-1
Online ISBN: 978-3-319-42520-7
eBook Packages: EngineeringEngineering (R0)