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Identification of general fuzzy measures by genetic algorithms based on partial information

Ting-Yu Chen, Jih-Chang Wang, Gwo-Hshiung Tzeng
2000 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
This study develops an identification procedure for general fuzzy measures using genetic algorithms.  ...  The experimental analysis indicates that using genetic algorithms to determine general fuzzy measures can obtain satisfactory results under the framework of partial information.  ...  Design of Experimental Data The experiment illustrates the identification of general fuzzy measures using genetic algorithms for .  ... 
doi:10.1109/3477.865169 pmid:18252383 fatcat:d5ex5tqqxzafff5yq7jz5ch74e

Effective Gas Identification Model based on Fuzzy Logic and Hybrid Genetic Algorithms

Yonug-Keun Bang, Hyung-Gi Byun, Chul-Heui Lee
2012 Journal of Sensor Science and Technology  
Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively.  ...  First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions.  ...  Byun is thankful for the financial support from the Converging Research Center Program funded by the Ministry of Education. Science and Technology (2011K000699).  ... 
doi:10.5369/jsst.2012.21.5.329 fatcat:batk3ipoqfae5pvxo66fwk7gaq

Genetic and Simulated Annealing Algorithms-based Traffic State Identification

Yuan Yuan, Chunfu Shao, Shiping Zhang, Lamtam Mei
2018 IOP Conference Series: Earth and Environment  
We proposed SAGA-FCM clustering algorithm which is combined simulated annealing algorithm (SA) with genetic algorithm (GA) for urban expressway traffic state identification.  ...  Meanwhile, it can overcome the instability of FCM algorithm clustering center and it is easy to fall into the local extreme value and "premature" problem of genetic algorithm.  ...  Acknowledgements This paper was supported by the Hebei Natural Science Foundation of China (No. E2016513016).  ... 
doi:10.1088/1755-1315/170/3/032117 fatcat:envftdlav5bm3dxmsahgpjg24a

A Soft Computing Method for Efficient Modelling of Smart Cities Noise Pollution

Attila Nemes, Gyula Mester, Tibor Mester
2018 Interdisciplinary Description of Complex Systems  
The training data set is built from measured data, combined with carefully selected simulation data to ensure the completeness of the model and its numerical robustness.  ...  Genetic algorithms with objectives to minimise the maximum absolute identification error, the root mean square of the identification error, reduce model complexity and ensure maximal numerical robustness  ...  Genetic algorithms are known powerful tools for global nonlinear search, thus suitable for efficient preliminary identification of fuzzy membership parameters [5] and also capable of fuzzy structure  ... 
doi:10.7906/indecs.16.3.1 fatcat:zypqrhzq4rhz5adk7sf46rmxnm

The Modified DNA Identification Classification on Fuzzy Relation

Yu Jen Hu, Yuh Hua Hu, Jyh Bin Ke
2011 Applied Mechanics and Materials  
In our experiment, there are 40 training data which are artificial samples, and we verify the proposed method with 182 natural DNA sequences.  ...  The result showed the proposed method enhanced the accuracy of the classification of genes from 76% to 93%.  ...  Conclusion The experiment designed a set of algorithm with fuzzy method of classification for DNA sequences.  ... 
doi:10.4028/www.scientific.net/amm.48-49.1275 fatcat:du6cwegbnvhifn7zcz2a5tv2be

A Genetic Fuzzy System Based On Improved Fuzzy Functions

Asli Celikyilmaz, I. Burhan Turksen
2009 Journal of Computers  
The new fuzzy function approach optimized with genetic algorithms is proposed to replace the fuzzy operators and operations of FRBs and improve accuracy of the fuzzy models.  ...  The merit of the proposed fuzzy functions method is that the uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy  ...  Hence we propose a new evolutionary fuzzy system with improved fuzzy functions (EIFF) approach to optimize the system parameters with genetic algorithms.  ... 
doi:10.4304/jcp.4.2.135-146 fatcat:dtlzr4qvarcpnaiujixk6cdmhi

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

Satyam Paul, Raheleh Jafari
2018 Advances in Science, Technology and Engineering Systems  
The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the  ...  This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques.  ...  Apart from the positivity of genetic algorithm, the drawback of genetic algorithm is that it cannot be used alone as modeling techniques.  ... 
doi:10.25046/aj030540 fatcat:efsc2pnbw5hifdqfpnq3xiamvy

A New Framework for Anomaly Detection in NSL-KDD Dataset using Hybrid Neuro-Weighted Genetic Algorithm

Muneeshwari P, Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India., Kishanthini M, Department of Computer Science and Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamilnadu, India.
2020 Journal of Computational Science and Intelligent Technologies  
The system is based on the Hybrid Genetic Neuro-Weighted Algorithm (HNWGA).In this weighted genetic algorithm is used for the selection of features and in this work a neuro-genetic fuzzy classification  ...  With testing dataset, this work was able to use the NSL-KDD data collection, the binary and multiclass problems.  ...  The authors confirm that the data supporting the findings of this research are available within the article. FUNDING None.  ... 
doi:10.53409/mnaa.jcsit1105 fatcat:tumjbgodyfg3ph5md2oym355w4

System Identification and Modeling for Interacting and Non-Interacting Tank Systems Using Intelligent Techniques

NS Bhuvaneswari
2012 International Journal of Information Sciences and Techniques  
The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using  ...  System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity.  ...  System identification involves building a mathematical model of a dynamic system based on set of measured stimulus and response samples.  ... 
doi:10.5121/ijist.2012.2503 fatcat:eawzmyuoazg5zfgksdwpyxzpay

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.  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have helped greatly in improving the presentation of the paper.  ... 
doi:10.1155/2012/893807 fatcat:dzmftipjgneazhywprr5pujve4

System identification and modeling for interacting and non-interacting tank systems using intelligent techniques [article]

N. S. Bhuvaneswari, R. Praveena, R. Divya
2012 arXiv   pre-print
The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using  ...  System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity.  ...  System identification involves building a mathematical model of a dynamic system based on set of measured stimulus and response samples.  ... 
arXiv:1208.1103v1 fatcat:poq37wohuvd6pgqip72q75v5hi

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)  
The same observation can be made, empirically, using a system identification method, e.g., a derivative-based optimization method and the genetic algorithm.  ...  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  ...  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

Detection of the Driver's Mental Workload Level in Smart and Autonomous Systems Using Physiological Signals

Dan Wang, Yier Lin, Liang Hong, Ce Zhang, Yajie Bai, Zhen Zhen Bi, Naeem Jan
2022 Mathematical Problems in Engineering  
The driver's mental workload is classified and the mental workload prediction model is constructed on the basis of the combination of the Fuzzy Pattern Recognition Algorithm and Genetic Algorithm.  ...  In this paper, a method is proposed to assess the mental workload of drivers, combining real driver's physiological data with the speed of his/her vehicle.  ...  Introduction to Genetic Algorithm and Fuzzy Pattern Recognition Algorithm 2.1. Genetic Algorithm.  ... 
doi:10.1155/2022/5233257 fatcat:uex3muv2jbfrdnmtdqbwuufkn4

System identification of fuzzy cartesian granule feature models using genetic programming [chapter]

James F. Baldwin, Trevor P. Martin, James G. Shanahan
1999 Lecture Notes in Computer Science  
In this paper we present the G_DACG constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data.  ...  G_DACG combines the powerful optimisation capabilities of genetic programming with a rather novel and cheap fitness function which relies on the semantic separation of concepts expressed in terms of Cartesian  ...  We present an induction algorithm that extracts concepts from example data in terms of Cartesian granule fuzzy sets.  ... 
doi:10.1007/bfb0095073 fatcat:gj6om6bbgzdtzgkduulzvfhrwy

A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

Liyan Zhang, Jian Ma, Xiaofeng Liu, Min Zhang, Xiaoke Duan, Zheng Wang
2022 Tehnički Vjesnik  
Finally, the model is verified by the measured data. The convergence speed and clustering efficiency of parallel genetic fuzzy clustering and original fuzzy c-means clustering are compared.  ...  In this paper, a parallel genetic fuzzy clustering algorithm is proposed to overcome the shortcomings of the fuzzy c-means clustering algorithm.  ...  CONCLUSIONS Based on the measured data, a parallel genetic fuzzy clustering algorithm is established to divide the traffic state in this paper, and then the SVM model is used to distinguish the traffic  ... 
doaj:afd4973d2f1849cabc217c35e662c586 fatcat:chlvccm32zewlmwienjmyaxwe4
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