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Evolutionary Learning in Identification of Fuzzy Model of Air Flow Supply System

Arunas Lipnickas, Vidmantas Macerauskas, Vaclovas Kubilius
2005 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications  
Evolutionary learning and especially genetic optimisation algorithms have recently received a lot of research attention as tools for identifying fuzzy models of the systems.  ...  A genetic algorithm is utilised to find the premise structure of the rules, also to optimise fuzzy set membership functions and the consequent model structure of the rules at the same time.  ...  CV Flow R 1 ,R 3 R 1 R 4 R 2 R 3 ,R 4 R 3 R 4 R 4 R 4 R 4 MF CV MF Flow * * CONCLUSIONS In this paper we showed the synthesis of evolutionary learning and the fuzzy model for system identification.  ... 
doi:10.1109/idaacs.2005.282949 fatcat:reyu6fbzbfgs3befjzkkn7lvli

Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network

Yang-Yin Lin, Jyh-Yeong Chang, Chin-Teng Lin
2013 IEEE Transactions on Neural Networks and Learning Systems  
This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems.  ...  An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning.  ...  IEEE self-evolving fuzzy neural network (IRSFNN), for dynamic system identification and prediction.  ... 
doi:10.1109/tnnls.2012.2231436 pmid:24808284 fatcat:jpyx2lxwavajpkrdnkm72dgs4a

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

Erkan Kayacan, Erdal Kayacan, Mojtaba Ahmadieh Khanesar
2015 IEEE transactions on industrial electronics (1982. Print)  
rules for both the premise and consequent parts of the type-2 fuzzy neural networks.  ...  In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned.  ...  Fuzzy neural networks (FNNs) combine the capability of fuzzy reasoning to handle uncertain information and the capability of ANNs to learn from input-output data sets in modeling nonlinear dynamic systems  ... 
doi:10.1109/tie.2014.2345353 fatcat:swte6syr4favhgtue2qq5ts23q

Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches

Sunday Olusanya Olatunji, Lahouari Cheded, Wasfi G. Al-Khatib, Omair Khan
2013 Journal of Intelligent Learning Systems and Applications  
We propose here two novel classification approaches to this problem: one based on the use of the powerful type-2 fuzzy logic systems (type-2 FLS) and the other on the use of the discriminative sensitivity-based  ...  The use of prosodic features has been used in a plethora of practical applications, including speech-related applications, such as speaker and word recognition, emotion and accent identification, topic  ...  In this paper, we proposed two novel approaches, based on type-2 fuzzy logic systems (type-2 FLS) and the sensitivity-based linear learning method (SBLLM), to the identification of question and non-question  ... 
doi:10.4236/jilsa.2013.53018 fatcat:cf2o2w3gn5a3jbfyfqep77nzbm

Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

Luis Alfaro, Claudia Rivera, Jorge Luna-Urquizo, Elisa Castañeda, Francisco Fialho
2018 Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Innovation in Education and Inclusion"   unpublished
In the present work, we present a Fuzzy Neural System Model for online identification of Learning Styles which gives support for contents personalization.  ...  We proposal a Hybrid System model, in which techniques of Neural Networks, Fuzzy Logic and Case Based Reasoning are incorporated into the multiagent system.  ...  Section 3B presents a proposed Multiagent Adaptive e-Learning system architecture, and Section 3C describe a Neural Fuzzy system for online identification of learning styles, which is one of the main components  ... 
doi:10.18687/laccei2018.1.1.259 fatcat:nnfvb26kpzcmvl5vq6hhctj54m

Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms

W. Yu, X. Li
2004 IEEE transactions on fuzzy systems  
Stable learning algorithms for the premise and the consequence parts of fuzzy rules are proposed.  ...  In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem.  ...  The learning procedure of fuzzy neural networks can be regarded as a type of parameter identification.  ... 
doi:10.1109/tfuzz.2004.825067 fatcat:3uqhafvucrb5rlubiodihovdi4

Pressure Tracking Control of a Pneumatic Control System

Ming-Chang Shih, Jiann-Bang Lee
1999 Proceedings of the JFPS International symposium on fluid power  
The purpose of the paper is to research the pressure tracking control by using neuro fuzzy and identification control method; the learning efficiency can be improved with the identification method.  ...  The performance of the pressure control under variable volume is better with this method than those with other method.  ...  If the error function is defined between the output after identification yid and the actual output y, 3. 3 Learning rate Combing neuro fuzzy and fuzzy identification for the adjustment of learning.  ... 
doi:10.5739/isfp.1999.697 fatcat:rlvsjhspeffebkdwsx4pn6krya

A Simplified Method on Fuzzy Identification Algorithm and Its Applications to Modeling of a Municipal Refuse Incinerator
ファジィ同定アルゴリズムの簡略化法と都市ゴミ焼却炉のモデリング

Kazuo TANAKA, Manabu SANO, Kazuyuki SUZUKI
1992 Transactions of the Society of Instrument and Control Engineers  
This algorithm consists of four stages which realize structure identification and parameter identification of a fuzzy model.  ...  The Widrow-Hoff learning algorithm, which is a learning method of neural networks, is used for parameter identification of a fuzzy model. The aim of the first stage is to identify a linear model.  ...  Kang: Structure Identification of Fuzzy Model, FUZZY SETS AND SYSTEMS, 28, 15/ 33 (1989)  ... 
doi:10.9746/sicetr1965.28.1355 fatcat:xm3ejhbskrczrgengb45fjjzy4

Recurrent Neural Network Based Fuzzy Inference System For Identification And Control Of Dynamic Plants

Rahib Hidayat Abiyev
2007 Zenodo  
As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant.  ...  This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant.  ...  SIMULATIONS OF RECURRENT NEURO-FUZZY INFERENCE SYSTEMS A. Identification of Non-linear Systems The identification problem is finding relation between input and output of the system.  ... 
doi:10.5281/zenodo.1085664 fatcat:khnvja3sprgwfo6iw5lx3g6lum

A new neuro-fuzzy identification model of nonlinear dynamic systems

Minho Lee, Soo-Young Lee, Cheol Hoon Park
1994 International Journal of Approximate Reasoning  
A fuzzy system is composed of fuzzification of input, reasoning (or inference) by fuzzy rules, and defuzzification of fuzzy output.  ...  Computer simulation shows" that neuro-fuzzy identification is very effective in modeling the fuzzy system whose fuzzy rules can not be obtained easily.  ...  Figure 3 . 3 Neuro-fuzzy identification structure of dynamic systems.  ... 
doi:10.1016/0888-613x(94)90007-8 fatcat:wqtgykcjp5glbet3zlwuojonkq

Monotone data samples do not always produce monotone fuzzy if-then rules: Learning with ad hoc and system identification methods

Chin Ying Teh, Kai Meng Tay, Chee Peng Lim
2017 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone  ...  As such, monotone fuzzy rule relabeling is useful. Besides that, a constrained non-linear programming method for FIS modelling is suggested, as a variant of the system identification method.  ...  Monotone Data Samples Do Not Always Produce Monotone Fuzzy If-Then Rules: Learning with Ad hoc and System Identification Methods  ... 
doi:10.1109/fuzz-ieee.2017.8015386 dblp:conf/fuzzIEEE/TehTL17 fatcat:wr5x6tn7jbbxrlxc4lqhhktwkq

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Cheng-Jian Lin, Cheng-Hsien Lin, Jyun-Yu Jhang
2020 Electronics  
Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm.  ...  (3) it performs identification and prediction adaptively and effectively.  ...  Acknowledgments: The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan for financially supporting this research under Contract No.  ... 
doi:10.3390/electronics9040631 fatcat:k5guhzh66nabvdgwnem7hcs32m

Hydraulic Actuator Identification Using Interval Type-2Fuzzy Neural Networks

Mohsen Vatani
2012 International Journal of Information and Electronics Engineering  
Neural Networks (NN) and Fuzzy Logic are widely used in nonlinear system modeling and identification.  ...  Index Terms-Nonlinear systems, identification, type-2 fuzzy neural network, hydraulic actuator. I.  ...  New methods of system identification are required for such conditions and recently new identification methods have been proposed based on fuzzy logic, neural networks and wavelets [2] , [5] .  ... 
doi:10.7763/ijiee.2012.v2.160 fatcat:amnbbqe7jbgn3cedl7nnefvt2i

A review on type-2 fuzzy neural networks for system identification

Jafar Tavoosi, Ardashir Mohammadzadeh, Kittisak Jermsittiparsert
2021 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability.  ...  , and sorting of data to learn the T2F-NNs, is presented.  ...  In Thoma et al. (2010) , Wiener and Hammerstein block models are discussed to system identification. In Ruano (2005) fuzzy, neural and fuzzy neural models are discussed for system identification.  ... 
doi:10.1007/s00500-021-05686-5 pmid:33716561 pmcid:PMC7941344 fatcat:glxrdnqdjzbkxbiyhmd6otx5ja

Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study

R.H. Abiyev, O. Kaynak
2008 IEEE transactions on industrial electronics (1982. Print)  
In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules.  ...  The structure is tested for the identification and the control of the dynamic plants commonly used in the literature.  ...  In this paper, the combination of fuzzy logic, NNs, and wavelet technology are used to solve identification and control of dynamic systems.  ... 
doi:10.1109/tie.2008.924018 fatcat:v6ef2lh3h5a3dnufyu6efqbiqq
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