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Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction (2016)
Conference Proceeding
Eyoh, I., John, R., & de Maere, G. (2016). Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parame- ters of the IT2IFL... Read More about Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction.

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models (2016)
Conference Proceeding
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2016). An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

In this paper, an extended ANFIS architecture is proposed. By incorporating an extra layer for the fuzzification process, the extended architecture is able to fit both type-1 and interval type-2 models. The learning properties of the proposed archite... Read More about An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models.

A similarity-based inference engine for non-singleton fuzzy logic systems (2016)
Conference Proceeding
Wagner, C., Pourabdollah, A., McCulloch, J., John, R., & Garibaldi, J. M. (2016). A similarity-based inference engine for non-singleton fuzzy logic systems. doi:10.1109/FUZZ-IEEE.2016.7737703

In non-singleton fuzzy logic systems (NSFLSs) input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty such as sensor noise. The performance of NSFLSs in handling such uncertainties depends both on the actual input... Read More about A similarity-based inference engine for non-singleton fuzzy logic systems.

A data analysis framework to rank HGV drivers (2015)
Conference Proceeding
Figueredo, G. P., Quinlan, P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2015). A data analysis framework to rank HGV drivers

We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top... Read More about A data analysis framework to rank HGV drivers.

Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing (2015)
Conference Proceeding
Tyasnurita, R., Özcan, E., Shahriar, A., & John, R. (2015). Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing

A hyper-heuristic is a heuristic optimisation method which generates or selects heuristics (move operators) based on a set of components while solving a computationally difficult problem. Apprenticeship learning arises while observing the behavior of... Read More about Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing.

A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation (2015)
Conference Proceeding
He, F., Qu, R., & John, R. (2015). A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation

In this paper we investigate a multi-objective portfolio selection model with three criteria: risk, return and liquidity for investors. Non-probabilistic uncertainty factors in the market, such as imprecision and vagueness of investors’ preference an... Read More about A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation.

A simulated annealing approach to supplier selection aware inventory planning (2015)
Conference Proceeding
Turk, S., Miller, S., Özcan, E., & John, R. (2015). A simulated annealing approach to supplier selection aware inventory planning. In 2015 IEEE Congress on Evolutionary Computation (CEC)doi:10.1109/CEC.2015.7257105

Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. Also, appropriate inventory management is critical to the success of a supply chain operation. In recent years, there has been a gro... Read More about A simulated annealing approach to supplier selection aware inventory planning.

Fuzzy adaptive parameter control of a late acceptance hyper-heuristic (2014)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. I. (2014). Fuzzy adaptive parameter control of a late acceptance hyper-heuristic. In 2014 14th UK Workshop on Computational Intelligence (UKCI)

A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of t... Read More about Fuzzy adaptive parameter control of a late acceptance hyper-heuristic.

Interval type-2 fuzzy sets in supplier selection (2014)
Conference Proceeding
Turk, S., John, R., & Özcan, E. (2014). Interval type-2 fuzzy sets in supplier selection. In 2014 14th UK Workshop on Computational Intelligence (UKCI)doi:10.1109/UKCI.2014.6930168

Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. This study introduces a model for solving the supplier selection problem using interval type-2 fuzzy sets. Moreover, the influence o... Read More about Interval type-2 fuzzy sets in supplier selection.

Quantification of perception clusters using R-fuzzy sets and grey analysis
Conference Proceeding
Khuman, A. S., Yang, Y., John, R., & Liu, S. (in press). Quantification of perception clusters using R-fuzzy sets and grey analysis

This paper investigates the use of the R-fuzzy significance measure hybrid approach introduced by the authors in a previous work; used in conjunction with grey analysis to allow for further inferencing, providing a higher dimension of accuracy and un... Read More about Quantification of perception clusters using R-fuzzy sets and grey analysis.


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