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Intelligent Evolutionary Algorithm for Fuzzy Programming Based on Nonlinear Support Vector Machine

2017 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
Finally, the fuzzy programming model based on the improved intelligent evolutionary algorithm is fitted and optimized by nonlinear support vector machine.  ...  In order to overcome this defect, this paper presents a hybrid intelligent evolutionary algorithm based on nonlinear support vector machine (SVM) to solve the fuzzy programming problem.  ...  FUZZY PROGRAMMING MODEL BASED ON IMPROVED INTELLIGENT EVOLUTION Fuzzy Programming Model based on Genetic Algorithm Fuzzy programming is a class of uncertain programming with fuzzy parameters.  ... 
doi:10.21311/001.39.10.20 fatcat:ns54ijbztnb55gbuyqlpezxdfu

Genetic Algorithm Optimization of SoS Meta-Architecture Attributes for Fuzzy Rule Based Assessments

Andrew Renault, Cihan Dagli
2016 Procedia Computer Science  
The initial population of suitable KPAs is reduced through non-derivative based optimization employed by a genetic algorithm (GA) that generates the ideal KPA candidates though optimal rank selection.  ...  A Mamdani-type rule based fuzzy inference system (MRBFIS) is then used to make a fuzzy assessment of the SoS meta-architecture concept using GA optimized and assessed KPAs as MRBFIS inputs.  ...  KPA chromosome population and genetic algorithm rank criteria Figure 4 . 4 SoS meta-architecture GA optimization of KPA chromosome using the rank selection method Figure 5 . 5 (a) Generalized bell  ... 
doi:10.1016/j.procs.2016.09.298 fatcat:lvxjtf5xpbco5f7qvwtqck27dm

A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems

Bin Zhang, Yung C. Shin
2020 Applied Sciences  
Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based  ...  The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear  ...  The derived fuzzy optimal controller applied to a nonlinear flexible-joint robot system and compared with the alternative trol technique, which demonstrated the effectiveness of the proposed method for  ... 
doi:10.3390/app11010062 fatcat:y4viby7qgvbo3el5kv2gyumdcy

Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing

Philip Doganis, Alex Alexandridis, Panagiotis Patrinos, Haralambos Sarimveis
2006 Journal of Food Engineering  
In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models.  ...  The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA).  ...  The GA-RBF algorithm The GA-RBF algorithm provides a complete framework for building time series models based on available data, since apart form providing a mathematical expression, it also selects the  ... 
doi:10.1016/j.jfoodeng.2005.03.056 fatcat:p4gjdxnifzhxfioggjax3tgbke

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Łukasz Bartczuk, Andrzej Przybył, Krzysztof Cpałka
2016 International Journal of Applied Mathematics and Computer Science  
This gives us very rich possibilities for exploring and interpreting the operation of the modelled system.  ...  Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules.  ...  Acknowledgment The authors would like to thank the reviewers for very helpful suggestions and comments in the revision process.  ... 
doi:10.1515/amcs-2016-0042 fatcat:ksvszkwm3banddfwupa5omhxfa

GA-based intelligent digital redesign of fuzzy-model-based controllers

Wook Chang, Jin Bae Park, Young Hoon Joo
2003 IEEE transactions on fuzzy systems  
The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.  ...  In this paper, authors present a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs).  ...  In this paper, we propose a GA-based intelligent digital redesign method for the digital control of complex continuous-time nonlinear systems with the TS fuzzy models and fuzzy-model-based controllers.  ... 
doi:10.1109/tfuzz.2002.806315 fatcat:mgkhywsiljhx5of2da2l37ieke

Self-Organizing Multi-layer Fuzzy Polynomial Neural Networks Based on Genetic Optimization [chapter]

S. K. Oh, W. Pedrycz, H. K. Kim, J. B. Lee
2004 Lecture Notes in Computer Science  
The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning.  ...  In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its  ...  For the optimization of the FPNN model, GA uses the serial method of binary type, roulette-wheel used in the selection process, one-point crossover in the crossover operation, and a binary inversion (complementation  ... 
doi:10.1007/978-3-540-24687-9_23 fatcat:2qo6fjm2hbfu3hezwqe2bysdcu

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

Ali Roshanzamir, Farzin Piltan, Narges Gholami mozafari, Azita Yazdanpanah, Marjan Mirshekari
2014 International Journal of Modern Education and Computer Science  
To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used.  ...  Index Terms-PID Controller, Fuzzy logic methodology, Adaptive Techniques, Model reference fuzzy tuning. Design Intelligent Model base Online Tuning Methodology for Nonlinear System  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their careful reading of this paper and for their helpful comments.  ... 
doi:10.5815/ijmecs.2014.04.07 fatcat:qocdv6b7efgrnk6jnooronjy6m

Evolutionary Tuning of Fuzzy Rule Base Systems for Nonlinear System Modelling and Control

Pintu Chandra Shill
2012 International Journal of Computer Science & Information Technology (IJCSIT)  
This paper also presents new flexible encoding method methods for evolutionary algorithms.  ...  flexible fuzzy models and controller for complex systems.  ...  This fitness function provides a means for evaluating the performance of fuzzy model with the selected fuzzy rule base in the process of evolution, so that an optimized fuzzy model or fuzzy controller  ... 
doi:10.5121/ijcsit.2012.4511 fatcat:qfrss5cvgzhnvduxsxb75vk42u

Tuning of a neuro-fuzzy controller by genetic algorithm

Teo Lian Seng, M. Bin Khalid, R. Yusof
1999 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA.  ...  Due to their powerful optimization property, genetic algorithms (GA's) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems.  ...  [19] used GA to tune a type of RBF based fuzzy model, with only three fuzzy memberships for each fuzzy variable.  ... 
doi:10.1109/3477.752795 pmid:18252294 fatcat:pzzirrskhfcetg77fayj3q33wu

Genetic Fuzzy Controller for a Gas-Turbine Fuel System [chapter]

Andrew Vick, Kelly Cohen
2012 Advances in Intelligent and Autonomous Aerospace Systems  
A fuzzy logic controller structure was developed for providing closed loop fuel control in the gas turbine application, using a genetic algorithm to tune the system to provide an accurate and fast response  ...  Information on genetic algorithms is presented, along with a study on how this optimization approach can be utilized to enhance the fuzzy logic controller process.  ...  Use of genetic algorithms allows for an easier method for tuning the various fuzzy system parameters (membership functions and rule base) to generate an accurate model of the pressure cycle that was robust  ... 
doi:10.2514/5.9781600868962.0229.0272 fatcat:rmrmyfvxdnc7lledgp4uj5ac6e

Evolutionary fuzzy control of flexible AC transmission system

Chun-Feng Lu, Chia-Feng Juang
2005 IEE Proceedings - Generation Transmission and Distribution  
A fuzzy-controller design by the hybrid of genetic algorithm and particle-swarm optimisation (F-HGAPSO) is employed for a thyristor-controlled series capacitor (TCSC) to improve the transient stability  ...  F-HGAPSO introduces the concept of the maturing phenomenon in nature into the evolution of individuals originally modelled by a genetic algorithm.  ...  To select parents for the crossover operation, the tournament-selection scheme is used, in which two enhanced elites are selected at random, and their fitness values are compared to select the elite with  ... 
doi:10.1049/ip-gtd:20045052 fatcat:xdqju6sdjjcjxdbzr7nuru2nua

Single Link Manipulator Trajectory Tracking using Nonlinear Control Algorithm

Musadaq Ahmed Hadi, Hazem I. Ali
2021 مجلة النهرين للعلوم الهندسية  
This algorithm is based on new techniques and methods in order to obtain a controller for the SLM system.  ...  A new robust control algorithm is proposed for a class of nonlinear systems represented by a Single Link Manipulator (SLM) system.  ...  An adaptive fuzzy control with indirect method was proposed for a class of driven flexible link robot manipulators using a new estimation technique to estimate the uncertainties [5] .  ... 
doi:10.29194/njes.24010030 fatcat:notrc4ibjfaori6y5hno24wqem

Using GA for Optimization of the Fuzzy C-Means Clustering Algorithm

Mohanad Alata, Mohammad Molhim, Abdullah Ramini
2013 Research Journal of Applied Sciences Engineering and Technology  
Fuzzy C-Means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition.  ...  The above mentioned approach is tested on the generated data from the original function and optimal fuzzy models are obtained with minimum error between the real data and the obtained fuzzy models.  ...  ACKNOWLEDGMENT The Authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding the work through the research group project No. RGP-VPP-036.  ... 
doi:10.19026/rjaset.5.5011 fatcat:2dqdjpg5vjd7dighft4ru5e2li

Genetically Dynamic Optimization Based Fuzzy Polynomial Neural Networks [chapter]

Ho-Sung Park, Sung-Kwun Oh, Witold Pedrycz, Yongkab Kim
2005 Lecture Notes in Computer Science  
The performance of the proposed gdFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.  ...  In this paper, we introduce a new architecture of genetically dynamic optimization based Fuzzy Polynomial Neural Networks (gdFPNN) and discuss its comprehensive design methodology involving mechanisms  ...  In this study, for the optimization of the FPNN model, GA uses the serial method of binary type, roulette-wheel used in the selection process, one-point crossover in the crossover operation, and a binary  ... 
doi:10.1007/11428831_98 fatcat:nhuqlt4mm5bbfn7ufoukhzfkgm
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