Self evolving Takagi-Sugeno-Kang fuzzy neural network.

Nam, Nguyen Ngoc

doi:10.32657/10356/50807

Cited by 2 publications

(4 citation statements)

References 128 publications

(197 reference statements)

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“…In Takagi-Sugeno fuzzy method the rules presented in the form functional binding (1) rather than as an output variable membership to the fuzzy sets ); 1 ,..., n x x -input variables; y -system output is a input variables function. Each rule will produce its own numerical value of the control action [3]. There are a number of curves typical forms to define the membership functions.…”

Section: Power Transformer Modelmentioning

confidence: 99%

Development of 110-220 kV Power Transformer Model for Equipment Functional State Assessment System

Khalyasmaa

Dmitriev

Kokin

et al. 2014

AMR

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This paper addresses the problems, connected with implementation of 110-220 kV power transformer structural model for automated equipment functional state assessment system based on test and technical diagnostics data. This article describes the basic construction principles of hybrid neural network using Takagi-Sugeno fuzzy method. The paper also provides the statistical data analysis results for power transformers (of real energy grid part) to define fuzzy neural network criteria (layers).

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Section: Power Transformer Modelmentioning

confidence: 99%

Development of 110-220 kV Power Transformer Model for Equipment Functional State Assessment System

Khalyasmaa

Dmitriev

Kokin

et al. 2014

AMR

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“…The Generic Self-Evolving Takagi-Sugeno-Kang or GSETSK (Nguyen, 2012;Nguyen et al, 2015) is proposed for this study. The self-evolving property of this model makes it suitable as a fully-online model which can work with data streams without any prior assumption of the data distribution.…”

Section: Gsetskmentioning

confidence: 99%

“…This class of DDM is a synergism of artificial neural networks (ANN) and fuzzy inference system (FIS) and is also referred to as neuro-fuzzy system (NFS) or fuzzy neural network (FNN). Much research has been done on this area in the last two decades resulting in the evolution of NFM from primitive static (such as the Adaptive-Network-based Fuzzy Inference System or ANFIS (Jang, 1993)) to selforganizing (such as the Dynamic Evolving Neural-Fuzzy Inference System or (DENFIS) (Kasabov and Song, 2002)) to self-evolving versions (such as Generic Self-Evolving Takagi-Sugeno-Kang or GSETSK (Nguyen, 2012;Nguyen et al, 2015)) (Figure 1.4). This research uses the state of the art self-evolving NFM.…”

Section: Introductionmentioning

confidence: 99%

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Online neuro-fuzzy models for real time flow forecasting

Ashrafi¹

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Forecasting with limited data or sparse data are two main challenges needed to be addressed. Data should be representative of the system under consideration when forecasting with traditional neuro-fuzzy models (NFMs); the condition which is not met in case of forecasting with limited data. Also traditional NFMs cannot handle sparse rule base; the condition which is not suitable for modeling the low-frequency events.Generic Self-Evolving Takagi-Sugeno-Kang or GSETSK is a state-of-the-art NFM with specific capabilities due to its specific clustering, rule pruning and optimization mechanisms. The fully online property allows the model to be used independent of historical data. Also its dynamic expanding-shrinking structure makes its rule base dynamic, compact, up-to-date and interpretable. GSETSK was run fully-online form the 1 st sample onwards for two real time hydrologic forecasting problems in this study. The two commonly encountered problems; rainfall-runoff and flow routing modeling represent different modelling complexities. The data used belongs to two totally different datasets including a catchment in Sweden and Lower Mekong River in South-east Asia. In this research firstly the capability of GSETSK in forecasting with large datasets were critically examined. GSETSK was evaluated against traditional NFMs including the local NFM, DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) and global NFM, ANFIS (Adaptive-Network-Based Fuzzy Inference System). Also the rule base dynamics of GSETSK were evaluated in relation to the complexity of the problem. Secondly the fully-online property of GSETSK was examined in forecasting with limited data e.g. forecasting in basins with limited data. Eventually for handling the sparse data, the Fuzzy Interpolative-Extrapolative Reasoning (FIER) was developed and imbedded in GSETSK.The overall findings of this research are as follows.

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Self evolving Takagi-Sugeno-Kang fuzzy neural network.

Cited by 2 publications

References 128 publications

Development of 110-220 kV Power Transformer Model for Equipment Functional State Assessment System

Development of 110-220 kV Power Transformer Model for Equipment Functional State Assessment System

Online neuro-fuzzy models for real time flow forecasting

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