IEEE Spectrum
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Journal of Machine Learning Research
A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets
Information Sciences: an International Journal
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
An improvement on genetic-based learning method for fuzzy artificial neural networks
Applied Soft Computing
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
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
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Sensitivity-based linear learning method (SBLLM) has recently been used as a predictive tool due to its unique characteristics and performance, particularly its high stability and consistency during predictions. However, the generalisation capability of SBLLM is sometimes limited depending on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. Since it made use of sensitivity analysis in relation to the data sets used, it is surely very prone to being affected by the nature of the dataset. In order to reduce the effects of uncertainties in SBLLM prediction and improve its generalisation ability, this paper proposes a hybrid system through the unique combination of type-2 fuzzy logic systems (type-2 FLSs) and SBLLM; thereafter the hybrid system was used to model PVT properties of crude oil systems. Type-2 FLS has been choosen in order to better handle uncertainties existing in datasets beyond the capability of type-1 fuzzy logic systems. In the proposed hybrid, 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 newly proposed T2-SBLLM hybrid system with each of the constituent type-2 FLS and SBLLM. Empirical results from simulation show that the proposed T2-SBLLM hybrid system has greatly improved upon the performance of SBLLM, while also maintaining a better performance above that of the type-2 FLS.