1 code implementation • 28 Feb 2022 • Themistoklis Botsas, Lachlan R. Mason, Omar K. Matar, Indranil Pan
In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowledge and intuition.
no code implementations • 7 Nov 2021 • Indranil Pan, Lachlan Mason, Omar Matar
Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders.
4 code implementations • 2 Aug 2020 • Themistoklis Botsas, Lachlan R. Mason, Indranil Pan
We introduce a novel rule-based approach for handling regression problems.
no code implementations • 23 Jul 2020 • Romit Maulik, Themistoklis Botsas, Nesar Ramachandra, Lachlan Robert Mason, Indranil Pan
We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.
1 code implementation • 10 Jul 2020 • Pavan Inguva, Lachlan Mason, Indranil Pan, Miselle Hengardi, Omar K. Matar
To reduce the required computational costs, we apply machine learning techniques for clustering and consequent prediction of the simulated polymer blend images in conjunction with simulations.
no code implementations • 13 Mar 2020 • Gabriel F. N. Gonçalves, Assen Batchvarov, Yuyi Liu, Yuxin Liu, Lachlan Mason, Indranil Pan, Omar K. Matar
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization.
no code implementations • 5 Feb 2018 • Daya Shankar Pandey, Saptarshi Das, Indranil Pan, James J. Leahy, Witold Kwapinski
In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor.
no code implementations • 28 Jan 2018 • Indranil Pan, Saptarshi Das
In a recent paper, we presented an intelligent evolutionary search technique through genetic programming (GP) for finding new analytical expressions of nonlinear dynamical systems, similar to the classical Lorenz attractor's which also exhibit chaotic behaviour in the phase space.
no code implementations • 29 Mar 2017 • Indranil Pan, Dirk Bester
In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach.
no code implementations • 29 Nov 2016 • Indranil Pan, Saptarshi Das
This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme.
no code implementations • 29 Nov 2016 • Indranil Pan, Saptarshi Das
The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation.
no code implementations • 29 Nov 2016 • Indranil Pan, Saptarshi Das
The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time.
no code implementations • 29 Nov 2016 • Indranil Pan, Saptarshi Das, Shantanu Das
In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives.
1 code implementation • 28 Oct 2016 • Indranil Pan, Dirk Bester
In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques.
no code implementations • 27 Sep 2014 • Indranil Pan, Saptarshi Das
In this paper, we propose a novel methodology for automatically finding new chaotic attractors through a computational intelligence technique known as multi-gene genetic programming (MGGP).
no code implementations • 3 Mar 2014 • Indranil Pan, Daya Shankar Pandey, Saptarshi Das
In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables.