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A neuro-fuzzy approach for estimation of time-to-flashover characteristic of polluted insulators

M. Savaghebi, A. Gholami, A. Jalilian, H. Hooshyar
2008 2008 IEEE 2nd International Power and Energy Conference  
In this paper, a neuro-fuzzy approach for estimation of time-toflashover of a polluted insulator under power frequency voltage is discussed.  ...  prerequisite is a representative sample of the function behavior.  ...  As seen, the neuro-fuzzy model has a higher accuracy. Also, run times of the neuro-fuzzy and neural network models using a 2.4GHz PC are 90s and 130s (for 5000 iterations), respectively.  ... 
doi:10.1109/pecon.2008.4762712 fatcat:pr3npm7tyrenxjbpoyvrghbste

The Prediction Of Air Pollution By Using Neuro-fuzzy Gmdh

A. Yousefpour, Z. Ahmadpour
2011 Journal of Mathematics and Computer Science  
GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network.  ...  This paper proposes a Neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH.  ...  The conclusion part of the fuzzy inference rule which infers output y is simplified as a real number w k .    K k k k w y 1  (3) This model is called the simplified fuzzy reasoning.  ... 
doi:10.22436/jmcs.02.03.13 fatcat:sbfoatenwneu7pravnoiq532a4

Page 92 of American Society of Civil Engineers. Collected Journals Vol. 126, Issue 2 [page]

2000 American Society of Civil Engineers. Collected Journals  
SIMPLIFIED FUZZY ARTMAP AS PATTERN RECOGNIZER By S. Rajasekaran,' Member, ASCE, and G. A.  ...  Paper No. 18628. 92 / JOURNAL OF COMPUTING IN CIVIL ENGINEERING / APRIL 2000 Therefore, the aim of this paper is to build a pattern rec- ognizer based on a fuzzy-neuro model and exhibit the follow- ing  ... 

Neuro-fuzzy Modeling and Fuzzy Rule Extraction Applied to Conflict Management [chapter]

Thando Tettey, Tshilidzi Marwala
2006 Lecture Notes in Computer Science  
A survey of all the pertinent literature relating to conflict management is also presented. The paper then introduces the Takagi-Sugeno fuzzy model for the analysis of interstate conflict.  ...  The Takagi-Sugeno model is found to be suitable for interstate modeling as it demonstrates good forecasting ability while offering a transparent interpretation of the modeled rules.  ...  To simplify the fuzzy rules and avoid the redundant fuzzy sets the number of inputs into the TS neuro-fuzzy model have been pruned down to four variables.  ... 
doi:10.1007/11893295_120 fatcat:jpayp45v3vbnhnif63iiww7xde

A neuro-fuzzy approach to data analysis of pairwise comparisons

H. Ichihashi, I.B. Türksen
1993 International Journal of Approximate Reasoning  
A simplified fuzzy reasoning model is obtained in the form of Gaussian radial basis functions.  ...  An iterative learning algorithm in fuzzy models, which is called neuro-fuzzy, has been recently developed within the framework of fuzzy modeling in the sense of M. Sugeno.  ...  This model is called simplified fuzzy model (Ichihashi and Watanabe [3, 4] ).  ... 
doi:10.1016/0888-613x(93)90011-2 fatcat:zwyosktnzzakrgm5yqefubzdwq

Performance Evaluation of Neuro-Fuzzy Clustering for Hydro Thermal Power System

2019 International Journal of Engineering and Advanced Technology  
Further PID and Neuro-Fuzzy Controllers are compared for these systems .  ...  Form simulation studies it is shown that the proposed Neuro-Fuzzy controller was able to attain a setting time of 10 Sec which is comparatively lower than other existing speed controllers.  ...  Perhaps the use of Neuro-Fuzzy controllers can be simplified by the advent of Hardware-In-Loop features in software like MATLAB.  ... 
doi:10.35940/ijeat.a1678.109119 fatcat:gmvfzdxyzjcjloakild7dlo3ni

Formalizing and solving the PM10 control problem

C. Carnevale, E. Pisoni, M. Volta
2008 IFAC Proceedings Volumes  
The nonlinear relationships linking air quality objective and precursor emissions are described by neuro-fuzzy models, identified through the processing of simulations of the TCAM deterministic multiphase  ...  modeling system, performed in the framework of the CityDelta-CAFE Project (EU 6th Framework Program).  ...  Source-receptor approach Simplified models based on neuro-fuzzy approach are identified through the processing of input and output of several runs of the GAMES deterministic modeling system.  ... 
doi:10.3182/20080706-5-kr-1001.02623 fatcat:6ipra3sdjzel3oztg4mrjdzx54

The development of an algorithmic model for object recognition from visual and sound information — Based on neuro-fuzzy logic

Shahnaz Shahbazova, Manfred Grauer, Musa Suleymanov
2011 2011 Annual Meeting of the North American Fuzzy Information Processing Society  
This paper considers the problem of recognizing the visual and sound information by constructing a virtual environment, which allows to qualitatively simplify the system and to carry out of experiments  ...  , and to create an algorithmic model of pattern recognition comparable to human capabilities.  ...  virtual environment, which allows to qualitatively simplify the system and to carry out of experiments, and to create an algorithmic model of pattern recognition comparable to human capabilities.  ... 
doi:10.1109/nafips.2011.5751923 fatcat:luw6d7r5zresxnizw6stt7noca

An Efficient Neuro-Fuzzy-Genetics Approach for Multi Criteria Decision Making

Chandrasekhar Mesh Ram, Shyam Sundar Agrawal
2015 International Journal of Hybrid Information Technology  
, which provides a better level of satisfaction to obtain the better decision.  ...  The present paper, we applied combined neural network, fuzzy logic and genetics algorithm approach to multi criteria decision-making in different areas.  ...  Yeh and Lee [8] showed the application of Neuro-Fuzzy hybrid modeling.  ... 
doi:10.14257/ijhit.2015.8.5.29 fatcat:ci363m2te5cp3mjarha6iedrxy

Effective Compressive Strengths of Corner and Edge Concrete Columns based on an Adaptive Neuro-Fuzzy Inference System

Hae-Chang Cho, Seung-Ho Choi, Sun-Jin Han, Sang-Hoon Lee, Heung-Youl Kim, Kang Su Kim
2020 Applied Sciences  
In this regard, this study proposed a method that can accurately estimate the effective compressive strengths by using an adaptive neuro-fuzzy inference system (ANFIS).  ...  In addition, parametric studies were performed using the ANFIS model, and a simplified equation for calculating the effective compressive strength was proposed, so that it can be easily used in practice  ...  of neuro-fuzzy system.  ... 
doi:10.3390/app10103475 fatcat:mikhzpvpqjeo7ogr3tb2ws5pvm

A Hybrid AI approach based on Genetic Algorithm and ANFIS for Heart Disease Diagnosis

Waheeda Rajab
2018 International Journal for Research in Applied Science and Engineering Technology  
Both datasets are modeled in a way to make them suitable for training and initial FIS model structure is generated.  ...  Besides the combination of simplified anfis and genetic algorithm, this work has also been carried out on a hybrid of GA-ANFIS-KFCM that shows better results than simplified ANFIS in terms of testing error  ...  In this work, it can be seen that the performance of a hybrid model of GA and simplified ANFIS is better than hybrid of neuro-fuzzy (ANFIS) model.  ... 
doi:10.22214/ijraset.2018.7133 fatcat:e7on25gonvb3xfqf2jk3ftvque

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Ryusuke Hata, M. A. H. Akhand, Md. Monirul Islam, Kazuyuki Murase
2018 International Journal of Intelligent Systems and Applications  
The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method.  ...  simplified fuzzy rules in conventional methods.  ...  An adaptive neuro-fuzzy system for building and optimizing fuzzy models has been proposed [14] . A variety of neuro-fuzzy methods are also proposed recently [15] [16] [17] [18] [19] [20] [21] .  ... 
doi:10.5815/ijisa.2018.05.01 fatcat:qp6nakkcvndetmbuisxwt4fxga

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Mohamed Mustafa MOHAMED IQBAL, Rayappan JOSEPH XAVIER, Jagannathan KANAKARAJ
2017 Turkish Journal of Electrical Engineering and Computer Sciences  
A neuro-fuzzy controller was developed using a hybrid learning algorithm and the effectiveness of the controller for all heavy-duty gas turbine plants (5, 6, 7, and 9 series) is demonstrated against load  ...  Various time domain specifications and performance index criteria of the neuro-fuzzy controller are compared with that of a fuzzy logic controller and an artificial neural network controller.  ...  The authors have developed a fixed gain and self-tuning proportional integral derivative (PID) controller for HDGT plants.  ... 
doi:10.3906/elk-1511-242 fatcat:5b6vwdbrjjdf3be22galtakcl4

Electricity Consumption Prediction Model Using Neuro-Fuzzy System

Rahib Abiyev, Vasif H. Abiyev, Cemal Ardil
2007 Zenodo  
As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus.  ...  The simulation of neuro-fuzzy system has been performed.  ...  As shown from table the performance of neuro-fuzzy prediction is better than other model.  ... 
doi:10.5281/zenodo.1330153 fatcat:qygydckc2baijix4xbdvvps3e4

Intelligent Neuro-Fuzzy Application in Semi-Active Suspension System [chapter]

Seiyed Hamid, Atabak Sarrafan, Meisam Abbasi, Amir Ali Akbar Khayyat
2012 Fuzzy Logic - Controls, Concepts, Theories and Applications  
First have brief reviewed on modelling of a full car model and third section clearly reveals more detailed information about neuro-fuzzy strategy for the full-car model.  ...  Fuzzy Logic -Controls, Concepts, Theories and Applications 238 determine voltage of the MR damper quickly and accurately, and the control effect of the neuro-fuzzy control strategy is better than that  ...  The neuro-fuzzy in fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model; and precise fuzzy modeling that is focused  ... 
doi:10.5772/35491 fatcat:pejslk33dra35erwrrucnfpuhq
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