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Modeling Identification of the Nonlinear Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm [chapter]

Ho Pham Huy Anh, Kyoung Kwan, Nguyen Thanh
2011 Robot Arms  
Each chromosome is reproduced with the probability of Modeling Identification of the Nonlinear Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm Fig. 1.  ...  Yes No Extinction strategy, k=0 www.intechopen.com Modeling Identification of the Nonlinear Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 7 Table 1.  ...  Modeling Identification of the Nonlinear Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm, Robot Arms, Prof.  ... 
doi:10.5772/16422 fatcat:rfa2htx2prcqpcvr3jxb5qtjwe

A Novel Identification Method for Generalized T-S Fuzzy Systems

Ling Huang, Kai Wang, Peng Shi, Hamid Reza Karimi
2012 Mathematical Problems in Engineering  
Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm.  ...  The simulation results show the effectiveness of the proposed algorithm.  ...  Parameter Estimation by Using Genetic Algorithm Genetic algorithm GA is a meta-heuristic method used to find a solution based on biological evolution process.  ... 
doi:10.1155/2012/893807 fatcat:dzmftipjgneazhywprr5pujve4

On Multidimensional Linear Modelling Including Real Uncertainty

Jana Nowakova, Miroslav Pokorny
2014 Advances in Electrical and Electronic Engineering  
To identify the fuzzy coefficients of model the genetic algorithm is used. The linear approximation of vague function together with its possibility area are analytically and graphically expressed.  ...  In the paper are defined vague data as specialized fuzzy sets -fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague regression parameters  ...  Genetic Algorithms Utilization As mentioned before, the classical method of linear programming used for the identification of fuzzy regression coefficients [11] was substituted by using a genetic algorithm  ... 
doi:10.15598/aeee.v12i5.1143 fatcat:4oaitcurized7mefde7zf4i7hi

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

L. M. Galantucci, G. Percoco, R. Spina
2004 International Journal of Advanced Robotic Systems  
The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products.  ...  This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs.  ...  A Fuzzy Genetic Algorithm (FGA) is a GA in which some algorithm components are implemented using fuzzy logic based tools, such as: fuzzy operators and fuzzy connectives for designing genetic operators  ... 
doi:10.5772/5622 fatcat:3ucxdpwdmbfvlf3hquqlxditz4

Page 1058 of American Society of Civil Engineers. Collected Journals Vol. 127, Issue 11 [page]

2001 American Society of Civil Engineers. Collected Journals  
control system using on-line process data.  ...  “Fuzzy controller design for municipal incinerators with the aid of genetic algorithms and genetic programming techniques.” Waste Mgmt. and Res., London, 18(5), 429- 443 Chang, N. B., Chen, W.  ... 

Page 7258 of Mathematical Reviews Vol. , Issue 2004i [page]

2004 Mathematical Reviews  
Leung [Kwong-Sak Leung], Fuzzy clustering method for content- based indexing (138-143); V. A. Oleshchuk, On-line fuzzy pattern matching on sequences (144-149); I.  ...  2004i:68003 Sugeno fuzzy systems (94-99); Farida Benmakrouha, Christiane Hespel and Edouard Monnier, Comparison of identification meth- ods based on fuzzy systems and an algebraic model (104-107); David  ... 

Self-Tuning Active Vibration Control in Flexible Beam Structures

M O Tokhi, M A Hossain
1994 Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering  
Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal  ...  MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used for AVC system design.  ...  Genetic Algorithms The conventional on-line system identification schemes such as least squares, instrument variable, maximum likelihood etc., are in essence local search techniques.  ... 
doi:10.1243/pime_proc_1994_208_339_02 fatcat:ad3a2utnyzhwxbtzhnjcbhlzae

Optimization of IG-Based Fuzzy System with the Aid of GAs and Its Application to Software Process [chapter]

Sung-Kwun Oh, Keon-Jun Park, Witold Pedrycz
2007 Lecture Notes in Computer Science  
The proposed fuzzy model implements system structure and parameter identification with the aid of IG and GAs. To identify the structure and the parameters of fuzzy model we use genetic algorithms.  ...  We introduce an optimization of information granules (IG)-based fuzzy model with the aid of genetic algorithms (GAs) to describe projects in terms of complexity and development time in experimental software  ...  Genetic algorithms were also used for further structural and parametric optimization of the fuzzy model.  ... 
doi:10.1007/978-3-540-72590-9_166 fatcat:hfbrqhkb7bcjrnczbd4whlqmtu

Comparative Performance of Intelligent Algorithms for System Identification and Control

M.A. Hossain, A.A.M. Madkour, K.P. Dahal, H. Yu
2008 Journal of Intelligent Systems  
Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal  ...  Finally a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments.  ...  The genetic algorithm is used based on the method of minimization of the prediction error [Tokhi and Hossain, 1997] .  ... 
doi:10.1515/jisys.2008.17.4.313 fatcat:h7wvp4wjjrd6vctiivcnarpzh4

Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm [chapter]

Hongkai Ding, Yunshi Xiao, Jiguang Yue
2005 Lecture Notes in Computer Science  
by sliding mode method 180 Design and Development of Fuzzy Control of Nitinol-Hydraulic Valve 181 MOTION ESTIMATION ALGORITHM BASED ON GENETIC SEARCH 182 Mixed Structured RBF Network for Direct Inverse  ...  a Combined Artificial Intelligent Approach In Distance Relay For Transmission line Protection In EPS 189 Advanced Distance Protection Scheme for Long Transmission Lines In Electric Power Systems Using  ... 
doi:10.1007/11539087_14 fatcat:q4v4h4mr7jcm5a7stk6cu35l4e

Dynamic fuzzy control of genetic algorithm parameter coding

R.J. Streifel, R.J. Marks, R. Reed, J.J. Choi, M. Healy
1999 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented.  ...  The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated.  ...  The fuzzy genetic algorithm parameter (GAP) coding methodology presented in this paper is specifically designed to improve the search performance on a parameter identification problem.  ... 
doi:10.1109/3477.764878 pmid:18252316 fatcat:vlojdf27yzglrip4g5ppvjvh6y

Modeling Nonlinear Systems by a Fuzzy Logic Neural Network Using Genetic Algorithms

Abdel-Fattah Attia, P. Horáček
2001 Acta Polytechnica  
This approach uses a Linear Adapted Genetic Algorithm (LAGA) to optimize the FLNN parameters.  ...  The constraints may be an indirect definition of the search ranges for every membership shape forming parameter based on 2nd order fuzzy set specifications.  ...  Reference data driven identification of parameters of fuzzy logic neural networks utilizing genetic algorithms has been proposed and tested.  ... 
doaj:8837ab4470c94fbd936fc4d861996fe1 fatcat:6nqlrozlqfhlfbbjree4ynqajm

Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Minrui Fei, S.L. Ho
1999 Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems. PEDS'99 (Cat. No.99TH8475)  
It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms  ...  Abstracli -In this paper, the eight kinds of on-line adaptive, learning ansd evolutionary strategies for fuzzy logic control are systematicallly introduced.  ...  GENETIC ALGORITHMS Genetic algorithms (GA's) are a class of search and optimization algorithms based on the observation of the natural process of evolution.  ... 
doi:10.1109/peds.1999.792863 fatcat:wjwgvmbyzzcadm7d74gqh7onoe

A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

T. Arul Raj, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.  ...  Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition.  ...  A Fuzzy Genetic Hybrid System is being developed to improve and model Genetic algorithms using fuzzy logic based techniques.  ... 
doi:10.17762/turcomat.v12i6.2416 fatcat:li7yi3oxqrfllggj3vu4sspfam

Modeling and OnLine Control of Nonlinear Systems using Neuro-Fuzzy Learning tuned by Metaheuristic Algorithms

Mourad Turki, Sana Bouzaida, Anis Sakly, Faouzi M'Sahli
2014 International Journal of Control and Automation  
and compare the obtained results of these methods applied in identification and on-line control of nonlinear systems with others methods in literature applied in the same problems.  ...  This new approach is applied to identify and control nonlinear systems with an on-line strategy.  ...  To establish the inverse model for control purpose, we use an on-line strategy.  ... 
doi:10.14257/ijca.2014.7.5.33 fatcat:zuvmxjn6krecjlaw3qib7q4kme
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