Journal ArticleDOI
Simplified Interval Type-2 Fuzzy Neural Networks
TLDR
A simple interval type-2 FNN, which uses intervaltype-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang type in the consequent of the fuzzy rule, which yields fewer test errors and less computational complexity than other type-1 fuzzy systems.Abstract:
This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.read more
Citations
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Journal ArticleDOI
Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems
TL;DR: Simulation results show that Generalized Type-2 Fuzzy Controllers outperform their Type-1 and Interval Type- 2 FBuzzy Controller counterparts in the presence of external perturbations.
Journal ArticleDOI
Recent advances in neuro-fuzzy system: A survey
K V Shihabudheen,G. N. Pillai +1 more
TL;DR: A review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017 is proposed to help readers have a general overview of the state-of-the-arts of neuro- fizzy systems and easily refer suitable methods according to their research interests.
Journal ArticleDOI
Evolving Type-2 Fuzzy Classifier
TL;DR: Numerical results demonstrate that the eT2Class produces more reliable classification rates, while retaining more compact and parsimonious rule base than state-of-the-art EFCs recently published in the literature.
Journal ArticleDOI
An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction
TL;DR: An evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems and is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems.
Journal ArticleDOI
An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm
TL;DR: Performance of metacognitive interval type-2 neurofuzzy inference system (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existingtype-2 and type-1 fuzzy inference systems.
References
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Book
Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions
TL;DR: This chapter discusses Type-2 Fuzzy Sets, a New Direction for FLSs, and Relations and Compositions on different Product Spaces on Different Product Spaces, as well as operations on and Properties of Type-1 Non-Singleton Type- 2 FuzzY Sets.
Journal ArticleDOI
Type-2 fuzzy sets made simple
Jerry M. Mendel,Robert John +1 more
TL;DR: Establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, and presenting a new representation for type- 2 fuzzy sets, and using this new representation to derive formulas for union, intersection and complement of type-1 fuzzy sets without having to use the Extension Principle.
Journal ArticleDOI
Interval type-2 fuzzy logic systems: theory and design
Qilian Liang,Jerry M. Mendel +1 more
TL;DR: An efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them is proposed.
Journal ArticleDOI
Type-2 fuzzy logic systems
TL;DR: A type-2 fuzzy logic system (FLS) is introduced, which can handle rule uncertainties and its implementation involves the operations of fuzzification, inference, and output processing, which consists of type reduction and defuzzification.
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