Computational intelligence

Kohonen, T., Spotting relevant information in extremely large document collections, Computational Intelligence Theory and Applications, Lecture Notes in Computer Science, (LNCS), Springer, Berlin, 1625, 59,1999. 

The financial support by the Deutsche Forschungsgemeinschaft (DFG) for the Collaborative Research Center Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods (Sonderforschungsbere-ich 531) at the Universitat Dortmund, Project CIO, is gratefully acknowledged. 

D. Dubois and H. Prade. Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 4 244-264, 1998. 

R. Xu, D. Wunsch, Clustering (IEEE Press Series on Computational Intelligence, Wiley, 2009)... 

Among the techniques of computational intelligence, ANNs attempt to mimic the structures and processes of biological neural systems. According to the type of available information, four types of models are used ... 

Kercel SW, Allgood GO, Dress WB, Hylton JO. An anticipatory model of cavitation. Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Applications and Science of Computational Intelligence II, Serial Number 0277-786X, 1999 6 224-235. 

De Castro LN, Timmis, J (2002) Artificial immune systems a new computational intelligence approach, Springer-Verlag, Berlin... 

Fu, L. (1994). Neural Networks in Computer Intelligence. McGraw-Hill, New York. 

Nauck, D. Kruse, R. Neurofuzzy systems for function approximation. Proceedings Fuzzy-Neuro Systems 97— Computational Intelligence 1997 Infix, Saint Augustin 316-323. 

Coello Coello, C. A. (2006). The EMOO repository a resource for doing research in evolutionary multiobjective optimization, IEEE Computational Intelligence Magazine 1, 1, pp. 37-45. 

Farina, M. (2002). A neural network based generalized response surface multiobjective evolutionary algorithm, in Proceedings of IEEE World Congress on Computational Intelligence (WCCI-2002) (Hawaii). 

Santana-Quinter, L. V., Serrano-Hernandez, V. A., Coello Coello, C. A., Hemandez-Diaz, A. G. and Mohna, J. (2007). Use of radial basis functions and rough sets for evolutionary multi-objective optimization, in Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM 2007) (IEEE Press, Honolulu, Hawaii, USA), pp. 107-114. 

Sathikannan, R. and Tan, K. C. (2001). Evolutionary Computation for Chemical Engineering Applications, Cotf. Computational Intelligence, Robotics and... 

EM Jordaan and GF Smits. Estimation of the regularization parameter for support vector regression. In Proc. World Conf. Computational Intelligence, pages 2785-2791, Honolulu, Hawaii, 2002. 

Computational intelligence is used to generate an algorithm that uses a matrix of weights with a series of inputs to derive an estimate of SoC. This method requires a minimum number of discrete data points, but it is possible through this technique to factor in the effects of age. 

Allen, J.N., Abdel-Aty-Zohdy, H.S., Ewing, R.L. Electronic nose inhibition in a spiking neural network for noise cancellation. In Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2004, pp. 129-133 (2004)... 

The advantage of LP methods for extracting spectroscopic information from spectra is exploited by Haselgrove and Elliott who developed a computer intelligence algorithm which is able to analyze a large quantity of data based on a user-defined pattern of expected components. 

Futschik, M. E., and Kasabov, N. K. (2002). Fuzzy clustering of gene expression data. In Proceedings of the World Congress of Computational Intelligence (WCCl), Hawai 2002, IEEE Press, Vol. 1, pp. 414-419. 

Micheh-Tzanakou, E. (2000). Supervised and Unsupervised Pattern Recognition Feature Extraction and Computational Intelligence, CRC Press, Boca Raton, FL. 

Kahraman, C. (2012). Computational intelligence systems in industrial engineering With recent theory and applications. London Springer. 

Secondly, there are machine learning techniques that rely on the collective computational intelligence paradigm, where a synergetic effect is expected from combining efforts of various agents. 

Apt, K.R., Rossi, F., Venable, K.B. CP-nets and Nash equilibria. In Proceedings of the 3rd International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS), pp. 13-16 (2005)... 

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