Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

Zhang, Guangrui; Mahfouf, Mahdi; Abdulkareem, Musa; Gaffour, Sidahmed; Yang, Yanyu; Obajemu, Olusayo; Yates, J. R.; Soberanis, Sabino Ayvar; Pinna, C.

doi:10.1016/j.asoc.2016.06.007

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…Introducing robustness and improved accuracy in the taxi-time prediction through using these T2FLSs can only be beneficial. When there is limited expert information, fuzzy logic systems can be identified using automatic rule generation methods such as those proposed in [3], [15], [18]. Usually, the parameters of the fuzzy logic system are tuned to minimise the root mean square error (RMSE) where the RMSE is defined as follows:…”

Section: A a Type-2 Fuzzy Modelmentioning

confidence: 99%

“…The second stage of the modelling framework involves including a Gaussian Mixture Modelling (GMM)-based error compensation strategy, which has been discussed in our earlier work in [15]. After the fuzzy modelling stage, for each sets of input variables, the residual errors can be obtained.…”

Section: B Uncertainty Modelling and The Error Compensation Strategymentioning

confidence: 99%

“…In this work, a hierarchical framework, where the taxi-time model is developed using a type-2 fuzzy logic system (T2FLS) to allow for handling linguistic uncertainties, is proposed. The second stage is an error compensation scheme similar to that developed in our earlier work in [15], which allows for uncertainty handling. To the best of our knowledge, this is the first time that such an approach has been used to address the above challenges implicit in the taxi-time prediction problem.…”

Section: Introductionmentioning

confidence: 99%

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A type-2 fuzzy modelling framework for aircraft taxi-time prediction

Obajemu

Mahfouf

Allerton

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Knowing aircraft taxi-time precisely a-priori is increasingly important for any airport management system. This work presents a new approach for estimating and characterising the taxi-time of an aircraft based on historical information. The approach makes use of the interval type-2 fuzzy logic system, which provides more robustness and accuracy than the conventional type-1 fuzzy system. To compensate for erroneous modelling assumptions, the error distribution of the model is further analysed and an error compensation strategy is developed. Results, when tested on a real data set for Manchester Airport (U.K.), show improved taxi-time accuracy and generalisation capability over a wide range of modelling assumptions when compared with existing fuzzy systems and linear regression-based methods.

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Section: A a Type-2 Fuzzy Modelmentioning

confidence: 99%

Section: B Uncertainty Modelling and The Error Compensation Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A type-2 fuzzy modelling framework for aircraft taxi-time prediction

Obajemu

Mahfouf

Allerton

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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“…Fuzzy logic was first introduced in 1965 in Zadeh's seminal paper [9]. It has found wide applicability ranging from control [10], signal processing [11] and modelling [12], [13]. Fuzzy modelling has been shown to be able to provide an intuitive representation of complex systems via if-then rules to which humans can relate and which makes them interpretable [14].…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients

Obajemu

Mahfouf

Catto

2018

IEEE Trans. Fuzzy Syst.

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This paper presents a new fuzzy modelling approach for analysing censored survival data and finding risk groups of patients diagnosed with bladder cancer. The proposed framework involves a new procedure for integrating the frameworks of interval type-2 fuzzy logic and Cox modelling intrinsically. The output of this synergistic framework is a score/prognostics index which is indicative of the patient's level of mortality risk. A threshold value is selected whereby patients with risk scores that are greater than this threshold are classed as high risk patients and vice versa.Unlike in the case of black-box type modelling approaches, the paper shows that interpretability and transparency are maintained using the proposed fuzzy modelling framework.Two data sets are used to test the modelling accuracy of the elicited models. The first is an artificial data set which has similar characteristics as in a typical survival data. The second relates to real-life bladder cancer data from which one requires a model that identifies the low risk and high risk patients and then recommends risk management decisions based on, predicted risk level, patient history and characteristics, disease pathology and event times. The performance of the proposed framework is compared with the traditional Cox model, logistic regression as well as a non-linear survival data modelling technique based on neural networks. This is the first time an attempt has been made to exploit the transparency advantages of fuzzy models and the principled statistical framework of the Cox model in order to identify risk groups and recommend risk management decisions from complex survival data sets. In both the artificial data and real data, the proposed modelling framework, although minimalistic, shows better generalisation performances than the previously reported models against which the results were compared.O. Obajemu and M. Mahfouf are with the

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“…Previous studies (Paalanen, 2004;Figueiredo and Jain, 2002;Gómez-Losada et al, 2014;Arellano and Dahyot, 2016) point out that any continuous distribution can be approximated arbitrarily well by a finite mixture of Gaussian distributions. Due to their usefulness as a flexible modeling tool, GMMs have received an increasing amount of attention from the academic community (Zhang et al, 2016;Khanmohammadi and Chou, 2016;Ju and Liu, 2012). In a d-dimensional space the Gaussian PDF is defined mathematically in the following form:…”

Section: Gaussian Mixture Model For Density Estimation 331 Gaussianmentioning

confidence: 99%

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui

Hoang

2017

Geosci. Model Dev.

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Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

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Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

Cited by 7 publications

References 14 publications

A type-2 fuzzy modelling framework for aircraft taxi-time prediction

A type-2 fuzzy modelling framework for aircraft taxi-time prediction

A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

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