Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis
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[1] Ingo Mierswa,et al. YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.
[2] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[3] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[4] Hod Lipson,et al. Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.
[5] Sanjeev S. Tambe,et al. Soft-sensor development for biochemical systems using genetic programming , 2014 .
[6] Stephen R. Marsland,et al. Machine Learning - An Algorithmic Perspective , 2009, Chapman and Hall / CRC machine learning and pattern recognition series.
[7] Jacek M. Zurada,et al. Introduction to artificial neural systems , 1992 .
[8] S. Ayatollahi,et al. Pressure and temperature functionality of paraffin-carbon dioxide interfacial tension using genetic programming and dimension analysis (GPDA) method , 2014 .
[9] M. Russo,et al. Genetic programming for photovoltaic plant output forecasting , 2014 .
[10] Miha Kovačič,et al. Genetic programming prediction of the natural gas consumption in a steel plant , 2014 .
[11] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[12] S. Zaidi. Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler , 2012 .
[13] David M. Skapura,et al. Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.
[14] Ovidiu Ivanciuc,et al. Applications of Support Vector Machines in Chemistry , 2007 .
[15] Tomás Cordero,et al. Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis , 2001 .
[16] Sanjeev S. Tambe,et al. Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier , 2014 .
[17] Athanasios Tsakonas,et al. Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation , 2011 .
[18] S. Channiwala,et al. A correlation for calculating elemental composition from proximate analysis of biomass materials , 2007 .
[19] Yinglong Wang,et al. Synthesis of heat-integrated complex distillation systems via Genetic Programming , 2008, Comput. Chem. Eng..
[20] John R. Koza,et al. Genetically breeding populations of computer programs to solve problems in artificial intelligence , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.
[21] S. S. Tambe,et al. Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms , 2013, BioEnergy Research.
[22] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[23] Vipin Kumar,et al. Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.
[24] J. H. Steiger. Tests for comparing elements of a correlation matrix. , 1980 .
[25] Jie Yu,et al. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..
[26] Salah Bouhouche,et al. Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process , 2010 .
[27] Sanjeev S. Tambe,et al. Soft-sensor development for fed-batch bioreactors using support vector regression , 2006 .
[28] Jianfeng Shen,et al. The prediction of elemental composition of biomass based on proximate analysis , 2010 .
[29] Shubh Bansal,et al. Support vector regression models for trickle bed reactors , 2012 .