Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
An Overview of Hybrid Neural Systems
Hybrid Neural Systems, revised papers from a workshop
Layered Hybrid Connectionist Models for Cognitive Science
Hybrid Neural Systems, revised papers from a workshop
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets
Information Sciences: an International Journal
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Letters: Fully complex extreme learning machine
Neurocomputing
Hybrid computational models for the characterization of oil and gas reservoirs
Expert Systems with Applications: An International Journal
Modeling Permeability Prediction Using Extreme Learning Machines
AMS '10 Proceedings of the 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation
Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems
Computers in Industry
Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems
Expert Systems with Applications: An International Journal
Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
A Class of Self-Stabilizing MCA Learning Algorithms
IEEE Transactions on Neural Networks
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Extreme learning machines (ELM), as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalisation capability of ELM often depends on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in ELM prediction and improve its generalisation ability, this paper proposes a hybrid system through a combination of type-2 fuzzy logic systems (type-2 FLS) and ELM; thereafter the hybrid system was applied to model permeability of carbonate reservoir. Type-2 FLS has been chosen to be a precursor to ELM in order to better handle uncertainties existing in datasets beyond the capability of type-1 fuzzy logic systems. The type-2 FLS is used to first handle uncertainties in reservoir data so that its final output is then passed to the ELM for training and then final prediction is done using the unseen testing dataset. Comparative studies have been carried out to compare the performance of the proposed T2-ELM hybrid system with each of the constituent type-2 FLS and ELM, and also artificial neural network (ANN) and support Vector machines (SVM) using five different industrial reservoir data. Empirical results show that the proposed T2-ELM hybrid system outperformed each of type-2 FLS and ELM, as the two constituent models, in all cases, with the improvement made to the ELM performance far higher against that of type-2 FLS that had a closer performance to the hybrid since it is already noted for being able to model uncertainties. The proposed hybrid also outperformed ANN and SVM models considered.