Pantula, P D and Miriyala, S S and Mitra, Kishalay
(2016)
KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery.
Materials and Manufacturing Processes.
ISSN 1042-6914
(In Press)
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Abstract
KERNEL – A novel parameter free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally estimate the configuration of ANFIS along with Sobol based fast sample size determination methodology. The proposed algorithm is capable of fine-tuning the existing knowledge base about the physics of the process in terms of human experience. It also enables knowledge discovery through a multi-objective optimization problem solved by NSGA-II, thus presenting machine invented physics of the process. Experimentally validated polymerization reaction network model is considered and ANFIS surrogates are built using KERNEL. Surrogate based optimization was found to be 9 times faster than conventional optimization using the time expensive model thus enabling its online implementation. Comparison of ANFIS with Kriging is also included.
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