scholarly journals Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers

2010 ◽  
Author(s):  
Abdullah Almaksour ◽  
Eric Anquetil
Keyword(s):  
Neuro Fuzzy ◽  
Fuzzy Classifiers ◽  
Premise Structure ◽  
Takagi Sugeno

scholarly journals The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

2019 ◽  
Vol 50 (4) ◽  
pp. 991-1001 ◽  
Author(s):  
Mohammad Ashrafi ◽  
Lloyd H. C. Chua ◽  
Chai Quek
Keyword(s):  
Fuzzy Inference System ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Training Dataset ◽  
Rule Base ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
River Routing

Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.

Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning

2013 ◽  
Vol 488 ◽  
pp. 17-32 ◽  
Author(s):  
Amin Talei ◽  
Lloyd Hock Chye Chua ◽  
Chai Quek ◽  
Per-Erik Jansson
Keyword(s):  
Online Learning ◽  
Fuzzy Model ◽  
Runoff Forecasting ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks

2008 ◽  
Vol 12 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Felix Pasila ◽  
◽  
Ajoy K. Palit ◽  
Georg Thiele ◽  
◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Average ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Training Algorithm ◽  
Electrical Load ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network

The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.

A Takagi–Sugeno type neuro-fuzzy network for determining child anemia

2011 ◽  
Vol 38 (6) ◽  
pp. 7415-7418 ◽  
Author(s):  
Novruz Allahverdi ◽  
Ayfer Tunali ◽  
Hakan Işik ◽  
Humar Kahramanli
Keyword(s):  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network
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