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A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

Kartick Subramanian, Ankit Kumar Das, Suresh Sundaram, Savitha Ramasamy
2013 Evolving Systems  
The cognitive component is an Interval Type-2 Neuro-Fuzzy Inference System (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism.  ...  A Meta-cognitive Interval Type-2 Neuro-Fuzzy Inference System (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper.  ...  Cognitive Component: Interval Type-2 Fuzzy Inference System The cognitive component of McIT2FIS is Interval Type-2 neuro-Fuzzy Inference System (IT2FIS), which is a six-layered neuro-fuzzy inference system  ... 
doi:10.1007/s12530-013-9102-9 fatcat:6e7wmcnxmnfnte7sdiwrtwtm5y

A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings [article]

Mainak Dan, Seshadhri Srinivasan
2018 arXiv   pre-print
A brief qualitative description of these algorithms namely meta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzy inference system (SAFIS) and evolving Takagi-Sugeno (ETS) model provide  ...  This investigation aims to study different adaptive fuzzy inference algorithms capable of real-time sequential learning and prediction of time-series data.  ...  Besides, the number of fuzzy rules used is also tabulated to reflect the architectural complexity of these inference systems during prediction.  ... 
arXiv:1809.08860v1 fatcat:suxi6tg3gbg2xnzsr7ieqpd7ny

2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25

2014 IEEE Transactions on Neural Networks and Learning Systems  
Chen, N., +, Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System.  ...  ., +, TNNLS Mar. 2014 571-584 A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System.  ...  The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014  ... 
doi:10.1109/tnnls.2015.2396731 fatcat:ztnfcozrejhhfdwg7t2f5xlype

A Fuzzy Skill Predictor for Early Childhood Educators

Moses Adah Agana, Ruth Wario
2018 International Journal of Engineering & Technology  
The skill prediction system was developed in two phases beginning with the generation of weighted fuzzy rules and then followed by the development of a fuzzy rule-based decision support system.  ...  The Mamdani Fuzzy inference model in MATLAB was used in implementing the system using weighted attributes of intelligence and ability to determine skills.  ...  Figure 1 : 1 Illustration of a Membership Function Figure 2 : 2 Data, Rules, Knowledge Base and Inference System of the Fuzzy Skill Prediction System for STE In figure 2 above, there are four inputs,  ... 
doi:10.14419/ijet.v7i3.19.16986 fatcat:ere2kartvrelvexenk5eocy6gm

Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees

Varun Kumar Ojha, Vaclav Snasel, Ajith Abraham
2018 IEEE transactions on fuzzy systems  
., a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs).  ...  This paper proposes a design of hierarchical fuzzy inference tree (HFIT).  ...  TSK FUZZY INFERENCE SYSTEMS A. Type-1 Fuzzy Inference Systems A TSK-type FIS is governed by the IF-THEN rules of the form [1] : R i : IF x 1 is A i1 AND . . .  ... 
doi:10.1109/tfuzz.2017.2698399 fatcat:u2xeuo2nfvhjhnjwqmnnzi5zca

PANFIS++: A Generalized Approach to Evolving Learning [article]

Mahardhika Pratama
2017 arXiv   pre-print
This module allows to actively select data streams for the training process, thereby expediting execution time and enhancing generalization performance, 2) PANFIS++ is built upon an interval type-2 fuzzy  ...  This is meant to tackle the temporal system dynamic.  ...  Some attempt has been devoted to actualise the evolving concept in the recurrent network structure and the interval-type 2 fuzzy system [21] - [25] .  ... 
arXiv:1705.02476v1 fatcat:wkwjlafrkfdsvlf62rslqviqqe

Metacognitive learning approach for online tool condition monitoring

Mahardhika Pratama, Eric Dimla, Chow Yin Lai, Edwin Lughofer
2017 Journal of Intelligent Manufacturing  
A novel tool condition monitoring approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm, recurrent  ...  Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts.  ...  It can be seen as a semi-supervised learner as a result of the online active learning scenario. • McITSFIS ) is a metacognitive classifier which combines the theory of interval type-2 fuzzy system into  ... 
doi:10.1007/s10845-017-1348-9 fatcat:una3j3iaufe3lnhxfttyylnot4

Heuristic design of fuzzy inference systems: A review of three decades of research

Varun Ojha, Ajith Abraham, Václav Snášel
2019 Engineering applications of artificial intelligence  
This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy  ...  The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugeno-Kang-type fuzzy systems.  ...  (b)Fuzzy inference system solution space where objective 1 is the error (1-accuracy) of the system and objective 2 is the complexity (interpretability) of the system.  ... 
doi:10.1016/j.engappai.2019.08.010 fatcat:nfyfauv2yjdxvbju2p37fkz2lq

A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams

Mahardhika Pratama, Jie Lu, Guangquan Zhang
2015 2015 IEEE International Conference on Systems, Man, and Cybernetics  
On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden  ...  Keywords-extreme learning machine, fuzzy neural network, metacognitive learning, evolving neuro fuzzy system, sequential learning. I.  ...  The RMI pruning method is pioneered in [14] , but, the type-2 version of RMI method is unexplored. In this paper, the RMI method is enhanced to deal with the interval type-2 fuzzy system.  ... 
doi:10.1109/smc.2015.487 dblp:conf/smc/PratamaLZ15 fatcat:45fgmbarsfhbrkuw7i5y4tmdum

Metacognitive Sedenion-Valued Neural Network and Its Learning Algorithm

Lyes Saad Saoud, Hasan Al-Marzouqi
2020 IEEE Access  
The Mc-SVNN contains two components: a sedenion-valued neural network that represents the cognitive component, and a metacognitive component, which serves to self-regulate the learning algorithm.  ...  In this article, a metacognitive sedenion-valued neural network (Mc-SVNN) and its learning algorithm are proposed. Its application to diverse time-series prediction problems is presented.  ...  , and the interval type-2 fuzzy inference system [14] .  ... 
doi:10.1109/access.2020.3014690 fatcat:cyzu5nqmxvgofmc33xsyflz4xq

PALM: An Incremental Construction of Hyperplanes for Data Stream Regression [article]

Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti, Matthew A. Garratt
2018 arXiv   pre-print
PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system.  ...  This figure can even double in the case of type-2 fuzzy system. In this work, a novel SANFS, namely parsimonious learning machine (PALM), is proposed.  ...  To solve the problem of uncertainty, temporal system dynamics and the unknown system order McSLM was extended in recurrent interval-valued metacognitive scaffolding fuzzy neural network (RIVMcSFNN) [11  ... 
arXiv:1805.04258v2 fatcat:k2k5k6tbzrfbzjkakols2brwfm

An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks

Mahardhika Pratama, Jie Lu, Edwin Lughofer, Guangquan Zhang, Meng Joo Er
2017 IEEE transactions on fuzzy systems  
The new recurrent network architecture evolves a generalized interval type-2 fuzzy rule, where the rule premise is built upon the interval type-2 multivariate Gaussian function, while the rule consequent  ...  Index Terms-evolving fuzzy systems, fuzzy neural networks, recurrent fuzzy neural networks, type-2 fuzzy systems, incremental learning, concept drifts I.  ...  This drawback has led to the notion of the interval type-2 fuzzy system [6] , which presents a simplified version of the type-2 fuzzy system.  ... 
doi:10.1109/tfuzz.2016.2599855 fatcat:6pvyhj22e5dsviht6aznjdqdzq

ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms

Andrius Lauraitis, Rytis Maskeliūnas, Robertas Damaševičius
2018 Journal of Healthcare Engineering  
We propose a hybrid (neurofuzzy) model that combines an artificial neural network (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of  ...  The feed-forward backpropagation (FFBP) neural network achieved the regression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject's reaction  ...  a fuzzy rules system.  ... 
doi:10.1155/2018/4581272 pmid:29713439 pmcid:PMC5866873 fatcat:ldtc3ypqavbybdwy2yiojsnxua

A Self-Adaptive Online Brain–Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System

Javier Andreu-Perez, Fan Cao, Hani Hagras, Guang-Zhong Yang
2018 IEEE transactions on fuzzy systems  
This paper presents a self-adaptive general type-2 fuzzy inference system (GT2 FIS) for online motor imagery (MI) decoding to build a brain-machine interface (BMI) and navigate a bi-pedal humanoid robot  ...  and scaling of the fuzzy system rules in an online BMI experiment with a real robot.  ...  [20, 21] , interval type-2 (IT2) fuzzy logic based systems outperformed conventional methods applied to BMI such as SVMs and LDA, as well as classic type-1 fuzzy inference systems (T1 FISs).  ... 
doi:10.1109/tfuzz.2016.2637403 fatcat:jj55zu7vdbcdhpfrtdaw3cyxma

Literature Review of various Fuzzy Rule based Systems [article]

Ayush K. Varshney, Vicenç Torra
2022 arXiv   pre-print
In this paper, we present an overview and literature review for various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), Hierarchical fuzzy system (HFS), neuro fuzzy  ...  Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent the human understandable knowledge.  ...  Researchers have been working towards extending type-1 HFS to the variants of type-2 HFS e.g., in [77] a variable selection method for an interval type-2 hierarchical fuzzy system has been presented;  ... 
arXiv:2209.07175v1 fatcat:zs6qimx4czhqhenk4phiasvkxa
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