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Found 16 papers, 4 papers with code
Rule-based Evolutionary Bayesian Learning
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.
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
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.
Rule-based Bayesian regression
We introduce a novel rule-based approach for handling regression problems.
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.
Numerical simulation, clustering and prediction of multi-component polymer precipitation
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.
Data-driven surrogate modelling and benchmarking for process equipment
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.
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor
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.
Evolving Chaos: Identifying New Attractors of the Generalised Lorenz Family
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.
Marginal likelihood based model comparison in Fuzzy Bayesian Learning
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.
Fractional Order AGC for Distributed Energy Resources Using Robust Optimization
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.
Fractional Order Load-Frequency Control of Interconnected Power Systems Using Chaotic Multi-objective Optimization
The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time.
Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO
This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme.
Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems
In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives.
Fuzzy Bayesian Learning
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.
When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming
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).
Global solar irradiation prediction using a multi-gene genetic programming approach
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.
