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An Adaptive Resource Allocating Neuro-Fuzzy Inference System with Sensitivity Analysis Resource Control [chapter]

Minas Pertselakis, Natali Raouzaiou, Andreas Stafylopatis
2009 IFIP Advances in Information and Communication Technology  
In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems.  ...  Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors.  ...  Introduction The guiding principle of soft computing is to exploit the tolerance for imprecision by devising methods of computation that lead to an acceptable solution at low cost [1] .  ... 
doi:10.1007/978-1-4419-0221-4_59 fatcat:xj37puf7o5fpzokd422mrkrpcy

Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Francisco Penedo, Rodolfo E. Haber, Agustín Gajate, Raúl M. del Toro
2012 IEEE Transactions on Industrial Informatics  
In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes.  ...  A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy  ...  Since 2005, he has been Member of the ASME Technical Committee for Model Identification and Intelligent Systems.  ... 
doi:10.1109/tii.2012.2205699 fatcat:hizn22uolvau5iwitchixbe5ai

A TSK-Type Neurofuzzy Network Approach to System Modeling Problems

C.-S. Ouyang, W.-J. Lee, S.-J. Lee
2005 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for system modeling problems.  ...  Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive singular value decompositionbased  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous referees for their constructive comments and suggestions.  ... 
doi:10.1109/tsmcb.2005.846000 pmid:16128458 fatcat:pqbaamdst5f47pjjyr5nlgq2qy

Soft Schemes for Earthquake-Geotechnical Dilemmas

Silvia García
2013 International Journal of Geophysics  
During the past years, soft computing techniques have been used for developing unconventional procedures to study earthquake geotechnical problems.  ...  Via the development of schemes for integrating data-driven and theoretical procedures, the soft computing tools are presented as reliable earthquake geotechnical models.  ...  Soft computing technologies are robust by design and operate by trading off precision for tractability.  ... 
doi:10.1155/2013/986202 fatcat:n42jtkk3arbg3izeainie3n4p4

Soft Computing Applications in Infrastructure Management

Gerardo W. Flintsch, Chen Chen
2004 Journal of Infrastructure Systems  
Fuzzy logic provides a methodology for approximate rea- soning and for computing with words; neural networks are effi- cient for curve fitting learning and system identification; genetic algorithms are  ...  fertile ground for the application of soft computing techniques as demonstrated by the many applications that have been reported in the literature.  ... 
doi:10.1061/(asce)1076-0342(2004)10:4(157) fatcat:7w5lwlicebedjbqizjzxwgoyyu

Handgrip Strength Evaluation Using Neuro Fuzzy Approach

Woo Chaw Seng, Mahsa Chitsaz
2010 Malaysian Journal of Computer Science  
The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks.  ...  The expert rules define the membership function for the fuzzy system. The fuzzy model based on the membership function, fed in by the neural network will intelligently classify the data.  ...  Lydia from Department of Orthopedic at Medical Faculty of University of Malaya for their help and idea sharing.  ... 
doi:10.22452/mjcs.vol23no3.3 fatcat:e3cag43kzfdjdfattcpeq4g5mu

March 2021: Top Read Articles in Soft Computing

2021 Zenodo  
Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications.  ...  The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years.  ...  identification.  ... 
doi:10.5281/zenodo.4607266 fatcat:ffsh3vr6lzfpzhs5gdtjqh4yiy

Adaptation of Fuzzy Inference System Using Neural Learning [chapter]

A. Abraham
2005 Studies in Fuzziness and Soft Computing  
models that have been evolved during the last decade.  ...  We present three different types of cooperative neurofuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters  ...  The third layer contains rule nodes that evolve through hybrid supervised/unsupervised learning.  ... 
doi:10.1007/11339366_3 fatcat:hyt6m7zr3bfnxjv24bxrwkz6la

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system

Sidra Mumtaz, Laiq Khan, Xiaosong Hu
2017 PLoS ONE  
Currently, in the energy market, hybrid power systems based on renewable energy have paved an attractive approach to produce electricity [2] .  ...  NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system.  ...  Acknowledgments The authors are highly grateful to Saghir Ahmed for providing valuable suggestions for improving and modifying the overall contents of the paper.  ... 
doi:10.1371/journal.pone.0173966 pmid:28329015 pmcid:PMC5362096 fatcat:krrtwonzhzgxvorfke3kdaubza

A Review of Image Denoising Methods

I. Irum, M. A. Shahid, M. Sharif, M. Raza
2015 Journal of Engineering Science and Technology Review  
Images can get corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods have been proposed.  ...  It is still a challenging and a hot problem for researchers.  ...  Neurofuzzy systems developed by combining the neural network and fuzzy systems have also been used for image denoising [172] .  ... 
doi:10.25103/jestr.085.07 fatcat:4c4f3oz3wzakhaitav3k5ktdp4

Hybrid soft computing systems for electromyographic signals analysis: a review

Hong-Bo Xie, Tianruo Guo, Siwei Bai, Socrates Dokos
2014 BioMedical Engineering OnLine  
Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis.  ...  With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose.  ...  The term soft computing was first proposed by Zadeh [27] for constructing a new generation of computational intelligent system.  ... 
doi:10.1186/1475-925x-13-8 pmid:24490979 pmcid:PMC3922626 fatcat:uifnqy6tmfe4nbnkif2fwmun44

Developing a Neuro-Fuzzy Model for Weather Prediction

Hatem Mohamed, Amr Ibrahim
2020 International Journal of Computer Applications  
Artificial intelligent [AI] especially, hybrid systems improve the performance of either pure neural network based or pure fuzzy logic based forecasting.  ...  In this study, a Neuro-fuzzy approach will be proposed to predict weather in Sadat region, western desert, Egypt.  ...  It was developed in [28] that a soft computing based methodology for the modeling of systems.  ... 
doi:10.5120/ijca2020919818 fatcat:nhrgyr437jdl5arki3zdu45gve

Evolutionary Neuro-Fuzzy Systems and Applications [chapter]

G. Castellano, C. Castiello, A. M. Fanelli, L. Jain
2007 Studies in Computational Intelligence  
Such hybrid methodologies retain limitations that can be overcome with full integration of the three basic soft computing paradigms, and this leads to evolutionary neural fuzzy systems.  ...  Evolutionary neural systems hybridise the neurocomputing approach with the solution-searching ability of evolutionary computing.  ...  Figure 1 shows their use in a Soft Computing investigation. In the following, after a description of the three basic Soft Computing paradigms, some hybrid soft computing systems are overviewed.  ... 
doi:10.1007/978-3-540-72377-6_2 fatcat:mesecd4qrfasjkxbalcpmpsnoe

A Cognitive Look at Geotechnical Earthquake Engineering: Understanding the Multidimensionality of the Phenomena [chapter]

Silvia Garcia
2012 Earthquake Engineering  
Such cooperation is of particular importance for constructing perception-based intelligent information systems.  ...  These SC paradigms will form the basis for creation and development of Computational Intelligence.  ...  the Neurofuzzy approach.  ... 
doi:10.5772/50369 fatcat:goelqzcd5jeqzmz6y4aqaxeb6a

Recent Literature

2007 Fuzzy sets and systems (Print)  
Stoll, A min-max approach to fuzzy clustering, estimation and identification, IEEE Trans. Fuzzy Systems 14(2) (2006) 248-262. S.L.  ...  Her- rera, Hybrid learning models to get the interpretability- accuracy trade-off in fuzzy modeling, Soft Comput. 10(9) (2006) 717-734. M. Eftekhari, M.J. Zolghadri, S.D.  ... 
doi:10.1016/j.fss.2007.01.002 fatcat:6d6kezwq2je25mb4us4ieruas4
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