KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery

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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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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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalay
Item Type: Article
Uncontrolled Keywords: ANFIS, Hypercube, Kriging, NSGA II, Optimization, Polymerization, Sobol, Surrogate
Subjects: Materials Engineering > Materials engineering
Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 24 Dec 2016 08:35
Last Modified: 10 Nov 2017 05:00
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