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Relevant bibliographies by topics / Fuzzy processes

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Academic literature on the topic 'Fuzzy processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Fuzzy processes"

1

Matłoka, Marian. "Convex fuzzy processes." Fuzzy Sets and Systems 110, no. 1 (February 2000): 109–14. http://dx.doi.org/10.1016/s0165-0114(98)00053-0.

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Chalco-Cano, Y., M. A. Rojas-Medar, and R. Osuna-Gómez. "s-Convex fuzzy processes." Computers & Mathematics with Applications 47, no. 8-9 (April 2004): 1411–18. http://dx.doi.org/10.1016/s0898-1221(04)90133-2.

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Matłoka, Marian. "h-PREINVEX FUZZY PROCESSES." Śląski Przegląd Statystyczny, no. 14 (2016): 27–39. http://dx.doi.org/10.15611/sps.2016.14.02.

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Stojaković, Mila. "Fuzzy martingales - a simple form of fuzzy processes∗." Stochastic Analysis and Applications 14, no. 3 (January 1996): 355–67. http://dx.doi.org/10.1080/07362999608809443.

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Shen, Qiang, Ruiqing Zhao, and Wansheng Tang. "Random fuzzy alternating renewal processes." Soft Computing 13, no. 2 (April 22, 2008): 139–47. http://dx.doi.org/10.1007/s00500-008-0307-y.

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Li, Shunqin, Qiang Shen, Wansheng Tang, and Ruiqing Zhao. "Random fuzzy delayed renewal processes." Soft Computing 13, no. 7 (September 20, 2008): 681–90. http://dx.doi.org/10.1007/s00500-008-0372-2.

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Kim, Byoung Kyun, and Jai Heui Kim. "Stochastic Integrals of Set-Valued Processes and Fuzzy Processes." Journal of Mathematical Analysis and Applications 236, no. 2 (August 1999): 480–502. http://dx.doi.org/10.1006/jmaa.1999.6461.

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Yuji Yoshida. "A time-average fuzzy reward criterion in fuzzy decision processes." Information Sciences 110, no. 1-2 (September 1998): 103–12. http://dx.doi.org/10.1016/s0020-0255(97)10079-2.

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Kaminskas, Vytautas, and Raimundas Liutkevičius. "Learning Fuzzy Control of Nonlinear Processes." Informatica 16, no. 4 (January 1, 2005): 571–86. http://dx.doi.org/10.15388/informatica.2005.116.

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Fann, W. R., and P. L. Hsu. "Fuzzy Adaptive Control of Milling Processes." IFAC Proceedings Volumes 25, no. 28 (October 1992): 88–92. http://dx.doi.org/10.1016/s1474-6670(17)49470-5.

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Dissertations / Theses on the topic "Fuzzy processes"

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Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.

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Kandiah, Sivasothy. "Fuzzy model based predictive control of chemical processes." Thesis, University of Sheffield, 1996. http://etheses.whiterose.ac.uk/3029/.

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The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

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Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the important unit operations for chemical industries, can be easily designed with the known composition values. Online measurements of the compositions can be done using direct composition analyzers. However, online composition measurement is not feasible, since, these analyzers, like gas chromatographs, involve large measurement delays. As an alternative, compositions can be estimated from temperature measurements. Thus, an online estimator that utilizes temperature measurements can be used to infer the produced compositions. In this study, ANFIS estimators are designed to infer the top and bottom product compositions in a continuous distillation column and to infer the reflux drum compositions in a batch distillation column from the measurable tray temperatures. Designed estimator performances are further compared with the other types of estimators such as NN and Extended Kalman Filter (EKF). In this study, ANFIS performance is also investigated in the adaptive Neuro-Fuzzy control of a pH system. ANFIS is used in specialized learning algorithm as a controller. Simple ANFIS structure is designed and implemented in adaptive closed loop control scheme. The performance of ANFIS controller is also compared with that of NN for the case under study.

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Teague, Karen J. "Fuzzy comprehensive evaluation (FCE) in military decision support processes." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/39023.

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Approved for public release; distribution is unlimited.
The United States has a tradition of military analysis using a federated or combined suite of models. However, these are not the only methods of modeling military problems. We consider the application and implications of foreign modeling approaches. The particular alternate technique we focus on is fuzzy comprehensive evaluation (FCE). FCE makes use of fuzzy mathematics, alone and in partnership with Analytic Hierarchy Process (AHP) models, to inform strategic and operational decisions. It is designed to aid leaders in capturing the complicated and sometimes fuzzy nature of multi-criteria decision problems through human knowledge and evaluations. These subjective inputs present criticisms regarding FCE solutions. FCE results are only as valid as the consistency of the subject matter experts opinions. Therefore, this thesis analyzes the FCE approach through a case study and evaluates the implications of FCE results when there is high variance in expert opinions.

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Jin, Gang-Gyoo. "Intelligent fuzzy logic control of processes with time delays." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388058.

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Van, Den Bosch Magali Marie. "Simulation of ion exchange processes using neuro-fuzzy reasoning." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/2161.

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Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.
Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.

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Beyan, Timur. "A New Fuzzy-chaotic Modelling Proposal For Medical Diagnostic Processes." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605924/index.pdf.

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Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.

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Tecle, Aregai, and Shafiu Jibrin. "Incorporating Fuzzy Logic and Stochastic Processes into Multiobjective Forest Management." Arizona-Nevada Academy of Science, 2011. http://hdl.handle.net/10150/296992.

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Sozio, John Charles. "Intelligent Parameter Adaptation for Chemical Processes." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/34089.

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Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.
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Petley, Gary John. "A method for estimating the capital cost of chemical process plants : fuzzy matching." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/11165.

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The purpose of this thesis is to improve the 'art' of early capital cost estimation of chemical process plants. Capital cost estimates are required in the early business planning and feasibility assessment stages of a project, in order to evaluate viability and to compare the economics of the alternative processes and operating conditions that are under consideration for the plant. There is limited knowledge about a new plant in the early stages of process development. Nevertheless, accurate cost estimates are needed to prevent incorrect decisions being made, such as terminating the development of a would-be profitable plant. The published early capital cost estimation methods are described. The methods are grouped into three types of estimate: exponent, factorial and functional unit. The performance of these methods when used to estimate the capital costs of chemical plants is assessed. A new estimating method is presented. This method was developed using the same standard regression techniques as used in the published methods, but derived from a new set of chemical plant data. The effect that computers have had on capital cost estimating and the future possibilities for the use of the latest computer techniques are assessed. This leads to the fuzzy matching technique being chosen to develop a new method for capital cost estimation. The results achieved when using fuzzy matching to estimate the capital cost of chemical plants are presented. These results show that the new method is better than those that already exist. Finally, there is a brief discussion of how fuzzy matching could be applied in the future to other fields of chemical engineering.

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Books on the topic "Fuzzy processes"

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1959-, Nishizaki Ichiro, and Katagiri Hideki, eds. Fuzzy stochastic multiobjective programming. New York: Springer, 2011.

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O, Esogbue Augustine, ed. Decision criteria and optimal inventory processes. Boston: Kluwer Academic, 1999.

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Kuiński, Jacek. Rozmyte procesy gałązkowe. Poznań: Wydawn. Politechniki Poznańskiej, 1988.

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Meier, Andreas, Edy Portmann, Kilian Stoffel, and Luis Terán, eds. The Application of Fuzzy Logic for Managerial Decision Making Processes. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54048-1.

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Liu, Baoding. Decision Criteria and Optimal Inventory Processes. Boston, MA: Springer US, 1999.

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Abdel-Kader, Magdy G. Investment decisions in advanced manufacturing technology: A fuzzy set theory approach. Brookfield, VT: Ashgate Pub., 1998.

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International Conference on Intelligent Processing and Manufacturing of Materials (2nd 1999 Honolulu, Hawaii). Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials: IPMM'99 : Hilton Hawaiian Village Hotel, Honolulu, Hawaii, July 10-15, 1999. Edited by Meech John A. Piscataway, N.J: IEEE, 1999.

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Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.

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Aliev, R. A. Fuzzy process control and knowledge engineering in petrochemical and robotic manufacturing. Köln: Verlag TÜV Rheinland, 1991.

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Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Book chapters on the topic "Fuzzy processes"

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Wang, Shuming, and Junzo Watada. "Fuzzy Stochastic Renewal Processes." In Fuzzy Stochastic Optimization, 55–82. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5_3.

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Harris, John. "Flow Processes." In An Introduction to Fuzzy Logic Applications, 10–36. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-9042-1_2.

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Harris, John. "Thermal Processes." In An Introduction to Fuzzy Logic Applications, 37–65. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-9042-1_3.

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Liu, Baoding, and Augustine O. Esogbue. "Fuzzy Criterion Decision Processes." In Decision Criteria and Optimal Inventory Processes, 125–58. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5151-5_8.

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Yoshida, Yuji. "Fuzzy Decision Processes With Expected Fuzzy Rewards." In International Series in Intelligent Technologies, 313–23. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5473-8_21.

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Zadrożny, Sławomir, Janusz Kacprzyk, and Zbigniew W. Raś. "Supporting Consensus Reaching Processes under Fuzzy Preferences and a Fuzzy Majority via Linguistic Summaries and Action Rules." In Consensual Processes, 289–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20533-0_16.

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Jones, A. "Fuzzy Subsets in Didactic Processes." In Fuzzy Sets Theory and Applications, 349–95. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4682-8_17.

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Traichel, Anke, Wolfgang Kästner, and Rainer Hampel. "Fuzzy Modeling of Dynamic Non-Linear Processes — Applied to Water Level Measurement." In Fuzzy Control, 361–75. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_33.

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Pieczynski, Andrzej, and Wolfgang Kästner. "Fuzzy Modelling of Multidimensional Non-linear Processes — Design and Analysis of Structures." In Fuzzy Control, 376–86. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_34.

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Dubois, Didier, and Henri Prade. "Fuzzy Sets and Possibility Theory : Some Applications to Inference and Decision Processes." In Fuzzy Logic, 66–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78023-3_4.

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Conference papers on the topic "Fuzzy processes"

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Voskoglou, Michael Gr. "Fuzzy assessment processes." In 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2015. http://dx.doi.org/10.1109/intelcis.2015.7397233.

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Fleury, Will. "Complex selection processes: Dealing with dependencies." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584198.

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de Souza, Lucas Botoni, Patrick Prieto Soares, Marcio Mendonca, Asmaa Mourhir, and Elpiniki I. Papageorgiou. "Fuzzy Cognitive Maps and Fuzzy Logic applied in industrial processes control." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491590.

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Reformat, Marek Z., and Ronald R. Yager. "Composition-based Users' matching processes with pythagorean fuzzy sets." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015747.

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Chan, K. Y., S. H. Ling, T. S. Dillon, and C. K. Kwong. "Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007321.

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Yager, Ronald R., Marek Z. Reformat, and Giray Gumrah. "Fuzziness, OWA and linguistic quantifiers for web selection processes." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007600.

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Moussa, Ahmed Shawky, Sherif AbdElazim Embaby, and Ibrahim Farag. "Intelligent real-time scheduling of dynamic processes in MPI." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015679.

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De Maio, Carmen, Giuseppe Fenza, Vincenzo Loia, Francesco Orciuoli, and Enrique Herrera-Viedma. "A Context-aware Fuzzy Linguistic Consensus Model supporting Innovation Processes." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737893.

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Cuong, Bui Cong, Pham Thanh Huyen, Pham Van Chien, and Pham Van Hai. "Some Fuzzy Inference Processes in Picture Fuzzy Systems." In 2019 11th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2019. http://dx.doi.org/10.1109/kse.2019.8919454.

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Vasickaninova, Anna, and Monika Bakosova. "Fuzzy control of integrating processes." In 2011 12th International Carpathian Control Conference (ICCC). IEEE, 2011. http://dx.doi.org/10.1109/carpathiancc.2011.5945891.

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