Theoretical Advances and Applications of Fuzzy Logic and Soft Computing

Nuclear engineering is one of the areas with a large potential for applications of fuzzy logic and intelligent computing, the development of which, however, is still in its infancy. The nuclear power industry requests special demands on plant safety, surpassing all other industries in its safety culture. Due to the public awareness of the risks of nuclear industry and the very strict safety regulations in force for nuclear power plants (NPPs), applications of fuzzy logic and intelligent computing in nuclear engineering present a tremendous challenge. The very same regulations prevent a researcher from quickly introducing novel fuzzy-logic methods into this field. On the other hand, the application of fuzzy logic has, despite the ominous sound of the word “fuzzy” to nuclear engineers, a number of very desirable advantages over classical methods, e.g., its robustness and the capability to include human experience into the controller. In this paper, we review some relevant applications of fuzzy logic and intelligent computing in nuclear engineering. Then, we present an on-going project on application of fuzzy logic control of the first Belgian Reactor (BR1) and other related applications of fuzzy logic at the Belgian Nuclear Research Centre (SCK•CEN). We conclude that research in fuzzy logic and intelligent computing has reached a degree where industrial application is possible. Investigations into this direction and particular in nuclear engineering are still very rare, but some existing results seem promising.KeywordsFuzzy LogicNuclear Power PlantFuzzy ControlFuzzy ControllerSteam GeneratorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This survey paper explores current and potential future uses of soft computing in aerospace applications. It starts with a brief introduction and definition of soft computing or computing for uncertainty. The main uses of soft computing are in control work. This paper discusses a few of the aerospace applications of soft computing which include Aircraft Sensor Management and Flight Control Law Reconfiguration, Multidisciplinary Aerospace Design, SR-30 Turbojet Applications and Aircraft Landing System Controller. Finally it will investigate other potential uses of soft computing technologies such as satellite data processing. Examples of literature being reviewed include “Fuzzy Logic, Neural Networks, and Soft Computing” by Lofti Zadeh (1994), Soft Computing Applications in Electrical Engineering by Devendra Chaturvedi (2008), Soft Computing and Intelligent Systems: Theory and Applications by Naresh Sinha, and Madan Gupta (2000) and “Soft Computing Applications in Aircraft Sensor Management and Flight Control Law Reconfiguration by Marcel Oosterom, Robert Babushka, and Henk B. Verbruggen (2002). In summary, soft computing methods can increase software quality and robustness in a wide area of aerospace and other technical applications, especially with advances in computational power.

Fuzzy-logic systems have been successfully developed for many industrial applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks.

Message from Editors-in-Chief

Soft Computing is a complex of methodologies that includes artificial neural networks, genetic algorithms, fuzzy logic, Bayesian networks, and their hybrids. It admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic the remarkable human capability of making decisions in real-life, ambiguous environments. Soft Computing has therefore become popular in developing systems that encapsulate human expertise. 'Applications of Soft Computing: Updating the State of Art' contains a collection of papers that were presented at the 12th On-line World Conference on Soft Computing in Industrial Applications, held in October 2007. This carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of soft computing and covers a wide range of application areas, including design, intelligent control, optimization, signal processing, pattern recognition, computer graphics, production, as well as civil engineering and applications to traffic and transportation systems. The book is aimed at researchers and practitioners who are engaged in developing and applying intelligent systems principles to solving real-world problems. It is also suitable as wider reading for science and engineering postgraduate students.

Book Review| December 01, 2005 Fuzzy Logic in Geology: Edited by Robert V. Demicco and George J. Klir Zhuoheng Chen Zhuoheng Chen 1Geological Survey of Canada (Calgary), 3303-33 Street NW, Calgary, AB, T2L 2A7 Search for other works by this author on: GSW Google Scholar Bulletin of Canadian Petroleum Geology (2005) 53 (4): 498–499. https://doi.org/10.2113/53.4.498 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Zhuoheng Chen; Fuzzy Logic in Geology: Edited by Robert V. Demicco and George J. Klir. Bulletin of Canadian Petroleum Geology 2005;; 53 (4): 498–499. doi: https://doi.org/10.2113/53.4.498 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Fuzzy Logic in Geology. 2004. Robert V. Demicco and George J. Klir (Eds.). Elsevier Academic Press, 347p. Price: $95 USD. For quite some time, the engineering community has enjoyed success in application of fuzzy logic, and has appreciated the resultant progress in understanding domain problems through its use. Most of us in the geological community perhaps did not fully recognize the impact of fuzzy logic in geology until recently, when Fuzzy Logic in Geology, edited by Professors Robert V. Demicco and George J. Klir, was published. Fuzzy set theory was introduced in 1965 by Proffesor Lotfi Zadeh of... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Soft computing is a set of inexact computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper will review the development of soft computing techniques, and a number of advanced soft computing techniques will be introduced. With these concepts and methods, applications of soft computing in the field of agricultural and biological engineering will be presented, especially in the soil and water context for crop management and decision support for precision agriculture. The future of development and application of soft computing in agricultural and biological engineering will be discussed.

Soft computing in medicine

Intelligent Engineering Systems

Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich knowledge representation, flexible knowledge acquisition, and flexible knowledge processing, which enable intelligent systems to be constructed at low cost. This paper reviews applications of SC in several industrial fields to show the various innovations by TR, HMIQ, and low cost in industries that have been made possible by the use of SC. Our paper intends to remove the gap between theory and practice and attempts to learn how to apply soft computing practically to industrial systems from examples/analogy, reviewing many application papers.

This paper presents an overview of soft computing techniques for reservoir characterization. The key techniques include neurocomputing, fuzzy logic and evolutionary computing. A number of documented studies show that these intelligent techniques are good candidates for seismic data processing and characterization, well logging, reservoir mapping and engineering. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty. Introduction Accurate prediction of reservoir performance is a difficult problem. This is mainly due to the failure of our understanding of the spatial distribution of lithofacies and petrophysical properties. Because of this, the recovery factors in many reservoirs are unacceptably low. The current technologies based on conventional methodologies are inadequate and/or inefficient. In this paper, we propose the next generation of reservoir characterization tools for the new millennium - soft computing1,2,3. Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. Soft computing is an ensemble of various intelligent computing methodologies which include neurocomputing, fuzzy logic and evolutionary computing. Unlike the conventional or hard computing, it is tolerant of imprecision, uncertainty and partial truth. It is also tractable, robust, efficient and inexpensive. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. Figure 1 shows schematically the flow of information and techniques to be used for intelligent reservoir characterization4,5,6,7. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation. This paper firstly outlines the unique roles of the three major methodologies of soft computing - neurocomputing, fuzzy logic and evolutionary computing. We will summarize a number of relevant and documented reservoir characterization applications. Lastly we will provide a list of recommendations for the future use of soft computing. This includes the hybrid of various methodologies (e.g. neural-fuzzy or neuro-fuzzy, neural-genetic, fuzzy-genetic and neural-fuzzy-genetic) and the latest tool of "computing with words" (CW)8. CW provides a completely new insight into computing with imprecise, qualitative and linguistic phrases and is a potential tool for geological modeling which is based on words rather than exact numbers. An appendix is also provided for introducing the basics in soft computing.

Applications of AI and soft computing for challenging problems in the oil industry

Human centricity and information granularity in the agenda of theories and applications of soft computing

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.