Abstract
This chapter presents the application of Computational Intelligence (CI) paradigms for supporting decision making processes. First, the three main CI techniques, i.e., evolutionary computing, fuzzy computing, and neural computing, are introduced. Then, a review of recent applications of CI-based systems for decision making in various domains is presented. The contribution of each chapter included in this book is also described. A summary of concluding remarks is presented at the end of the chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ai, L., Wang, J., Wang, X.: Multi-features fusion diagnosis of tremor based on artificial neural network and D–S evidence theory. Signal Processing 88, 2927–2935 (2008)
Benedetti, A., Farina, M., Gobbi, M.: Evolutionary multiobjective industrial design: the case of a racing car tire-suspension system. IEEE Trans. on Evolutionary Computation 10, 230–244 (2006)
Bezdek, J.C.: What is a computational intelligence? In: Zurada, J.M., Marks II, R.J., Robinson, C.J. (eds.) Computational Intelligence: Imitating Life, pp. 1–12. IEEE Press, Los Alamitos (1994)
Cebeci, U.: Fuzzy AHP-based decision support system for selecting ERP sys-tems in textile industry by using balanced scorecard. Expert Systems with Applications 36, 8900–8909 (2009)
Dağdeviren, M., Yavuz, S., Kılınç, N.: Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications 36, 8143–8151 (2009)
Hu, P.J.H., Wei, C.P., Cheng, T.H., Chen, J.X.: Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making. Decision Support Systems 43, 1226–1241 (2007)
Jarman, I.H., Etchells, T.A., Martin, J.D., Lisboa, P.J.G.: An integrated framework for risk profiling of breast cancer patients following surgery. Artificial Intelligence in Medicine 42, 165–188 (2008)
Marks, R.: Intelligence: computational versus artificial. IEEE Transactions on Neural Networks 4, 737–739 (1993)
Perng, Y.H., Juan, Y.K., Hsu, H.S.: Genetic-algorithm-based decision support for the restoration budget allocation of historical buildings. Building and Environment 42, 770–778 (2007)
Power, D.J.: Specifying an expanded framework for classifying and describing decision support systems. Communications of the Association for Information Systems 13, 158–166 (2004)
Rafiq, Y., Beck, M., Packham, I., Denhan, S.: Evolutionary computation and visualisation as decision support tools for conceptual building design. In: Topping, B.H.V. (ed.) Innovation in Civil and Structural Engineering Computing, pp. 49–74. Saxe-Coburg Publications (2005)
Übeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Computer Methods and Programs in Biomedicine 93, 313–321 (2009a)
Übeyli, E.D.: Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digital Signal Processing 19, 320–329 (2009b)
Wang, T.C., Lee, H.D.: Developing a fuzzy TOPSIS approach based on sub-jective weights and objective weights. Expert Systems with Applications 36, 8980–8985 (2009)
Wu, D.: Supplier selection in a fuzzy group setting: a method using grey related analysis and Dempster–Shafer theory. Expert Systems with Applications 36, 8892–8899 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jain, L.C., Lim, C.P. (2009). Advances in Decision Making. In: Rakus-Andersson, E., Yager, R.R., Ichalkaranje, N., Jain, L.C. (eds) Recent Advances in Decision Making. Studies in Computational Intelligence, vol 222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02187-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-02187-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02186-2
Online ISBN: 978-3-642-02187-9
eBook Packages: EngineeringEngineering (R0)