Journal Article10.1016/J.ASOC.2013.02.018
Improved sensitivity based linear learning method for permeability prediction of carbonate reservoir using interval type-2 fuzzy logic system
Sunday O. Olatunji,Ali Selamat,Abdul Azeez Abdul Raheem +2 more
- 01 Jan 2014
- Vol. 14, pp 144-155
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TL;DR: An improved sensitivity based linear learning method (SBLLM) model through the hybridization of type-2 fuzzy logic systems (type-2 FLS) and SBLLM is proposed and empirical results from simulation show that the proposed improved hybrid model has greatly improved upon the performance of the standard SB LLM.
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Abstract: This paper proposed an improved sensitivity based linear learning method (SBLLM) model through the hybridization of type-2 fuzzy logic systems (type-2 FLS) and SBLLM. The generalization abilities of the SBLLM often rely on whether the available dataset is free of uncertainties to ensure successful result, which means that its generalization capability is sometimes limited depending on the nature of the dataset. Type-2 FLS has been choosing in order to better handle uncertainties existing in datasets and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS). In the proposed method, the type-2 FLS is used to handle uncertainties in reservoir data so that the cleaned data from type-2 FLS is then passed to the SBLLM for training and then final prediction using testing dataset follows. Comparative studies have been carried out to compare the performance of the proposed hybrid system with that of the standard SBLLM. Empirical results from simulation show that the proposed improved hybrid model has greatly improved upon the performance of the standard SBLLM.
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Citations
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Medical data classification using interval type-2 fuzzy logic system and wavelets
Thanh Nguyen,Abbas Khosravi,Douglas Creighton,Saeid Nahavandi +3 more
- 01 May 2015
TL;DR: Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system.
128
On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation
TL;DR: The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base.
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A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir
Kabiru O. Akande,Taoreed O. Owolabi,Taoreed O. Owolabi,Sunday O. Olatunji,Abdulazeez Abdulraheem +4 more
TL;DR: The performance of particle swarm optimization (PSO) technique is investigated for the optimal selection of SVR hyper-parameters for the first time in modelling and characterization of hydrocarbon reservoir based on its superior performance over the commonly employed optimization techniques.
86
A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting
TL;DR: A novel design of interval type-2 fuzzy logic systems (IT2FLS) is presented by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting and the ELM strategy ensures fast learning of the IT1FLS as well as optimality of the parameters.
83
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