Application of Artificial Intelligence on Modeling and Optimization

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.

[1]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[2]  E. R. Benson,et al.  Neural Network Modelling and Sensitivity Analysis of a Mechanical Poultry Catching System , 2005 .

[3]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[4]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[5]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Shahaboddin Shamshirband,et al.  RETRACTED ARTICLE: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper , 2014, Applied Intelligence.

[7]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[9]  Bo Xing,et al.  Imperialist Competitive Algorithm , 2014 .

[10]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[11]  B. Karaağaç,et al.  Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .

[12]  Shervin Motamedi,et al.  Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology , 2015, Comput. Electron. Agric..

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.