Adaptive paths: application of fuzzy logic in the GIS analysis of the natural and cultural values of the cape Trafalgar surroundings (Cádiz, Spain)

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.

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.

The challenges involved in the application of fuzzy logic in wireless sensors networks often stem from the limitation in processing and storage capabilities of the nodes. This anomaly can be overcome by using a centralized data sink, equipped with more storage and processing capabilities and which can also serve as the decider on the occurrence or otherwise of the event of interest based on selected readings of a subset of the deployed nodes. It is known that selecting a finite subset of a universal set can be intractable especially with relatively large size of the problem space. In this paper, we propose the application of T-norm Fuzzy Logic(TFL) to address the sensor selection problem and compare its performance to that of a standard Genetic Algorithm (GA). Extensive simulation results reveal the usefulness of this approach and how it is closely related to the GA technique.

The chapter provides examples and applications based on fuzzy sets and fuzzy logic-based theory. Initially, basic and fundamental examples of day to day life are demonstrated in this chapter with necessary design details and step by step calculations. The examples included here are fuzzification of irregular students considering fuzzy attendance, speed of a vehicle, job selection, fuzzy operations for almond sorting, and viral disease diagnosis such as the Covid-19. Numeric examples of fuzzification, defuzzification, operations on fuzzy sets, fuzzy relations are also included. Applications of fuzzy logic in fashion designing, software engineering, domestic appliances such as washing machines, share market analysis, and sensor control are also discussed in this chapter. Detailed discussion is presented on restaurant menu planner and customized representation of material to slow learners by giving complete systems architectures, design of fuzzy functions, and fuzzy rules. Traditional fuzzy logic, which is known as type-1 fuzzy logic, has got some limitations. To overcome the limitations, type-2 fuzzy logic is used. This chapter introduces and demonstrates an application of type-2 fuzzy logic along with its membership function. The fuzzy logic as a constituent of computational intelligence evolves continuously and observes possibilities of many innovative research opportunities. Besides detailed discussion on approximately 20 examples as mentioned above, in the end, the chapter enlists possible research ideas in the pure fuzzy logic-based system. There are possibilities of hybrid and applied research in the field of fuzzy logic too, which are enlisted at the end of the chapter. Approximately 40 core research ideas/projects and applications, which will be helpful for the learners, professionals, and researchers, are contributed to this chapter.

This paper exemplifies the possibility of applying fuzzy logic into the process of decision making regarding the selection of executive managers. The decision making process related to the selection of executive managers has been conceived in such a way that human resource (HR) departments assess candidates with application of a grade system. Candidates can be assessed against defined mangers’ goals. Research concerning managers’ general goals was used for this paper and the goals which research has proved to be of the greatest relative weight were selected. The application of fuzzy logic, along with a multi-criteria analysis, is very convenient for decision making (selection of candidates, optimization of processes, choice of the optimal variant, etc) when there is vagueness, uncertainty and a great number of candidates. This paper discusses the process of making an optimal - preferential decision (choice of an optimal manager for leading positions in a company) by application of fuzzy logic and a fuzzy system.

This book comprises a selection of papers from the IFSA 2007 World Congress on theoretical advances and applications of fuzzy logic and soft computing. These papers were selected from over 400 submissions and constitute an important contribution to the theory and applications of fuzzy logic and soft computing methodologies. Soft Computing consists of several computing paradigms, including fuzzy logic, neural networks, genetic algorithms, and other techniques, which can be used to produce powerful intelligent systems for solving real-world problems. Applications range from pattern recognition to intelligent control and sow the advantages of using soft computing theory and methods. The papers of IFSA 2007 also make a contribution to this goal.

Performance management has become in a key success factor for any organization. Traditionally, performance management has focused uniquely in financial measures, mainly using quantitative measures, but two decades ago they were extended towards an integral view of the organization, appearing qualitative measures. This type of extended view and associated measures have a degree of uncertainty that needs to be bounded. One of the essential tools for uncertainty bounding is the fuzzy logic and, therefore,the main objective of this paper is the analysis of the literature about the application of fuzzy logic in performance measurement systems operating within uncertainty environments with the aim of categorizing, conceptualizing and classifying the works written so far. Finally, three categories are defined according to the different uses of fuzzy logic within performance management concluding that the most important application of fuzzy logic that counts with a higher number of studies is uncertainty bounding.

Nowadays, fuzzy logic applications can be found in a variety of fields, specially engineering and scientific areas. However, applications of fuzzy logic in the artistic fields are not abundant. This is highly surprising if we consider the fact that other related techniques such as neural networks and genetic algorithms have been widely used for artistic and creative purposes. Two applications of fuzzy logic in the artistic domain are presented here: a fuzzy logic-based mapping strategy for audiovisual composition and a novel audio synthesis technique based on sound particles and fuzzy logic.

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relationto any product, service, event or any person. One of the most common methods used in sentimentanalysis of text content is natural language processing. Sentiment analysis of natural languagetext can be assessed using numerous methodologies such as machine learning algorithms andstatistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic waschosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way ofdefining the problem leads to a reduction in bias, both positively and negatively. Secondly, learn ing approaches based on fuzzy rules are fundamentally different from those learning approachesthat are widely used in sentiment classification, such as support vector machines, naive Bayes,etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree towhich an instance belongs to each individual class. The proposed model for sentiment analysis oftext reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure ofthe evaluation of the indicators of the polarity of words. Word polarity values are obtained byapplying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were createddepending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparisonof the presented approach based on fuzzy logic with other dictionary-based methods demonstratesthe superiority of the developed models based on the application of fuzzy logic.

This research aims to eliminate uncertainty due to organoleptic quality assessment, with the application of fuzzy logic. A case study was carried out by measuring the quality of fish floss products in Ambon city. This organoleptic quality assessment is linguistic in nature, so the variables and parameter determination are uncertain. Because of the uncertainty of the variables and parameters used, one method that can be applied is to use fuzzy logic. The basis of fuzzy logic is the fuzzy set theory. In fuzzy set theory, the role of the degree of membership as a determinant of the existence of elements in a set is very important. The results indicate that fuzzy logic can be used to reduce uncertainty in the organoleptic analysis. The quality of fish floss is at the real value of 4.12. Thus, consumers generally assume that the quality of fish floss is good.

This research aims to reduce losses that arise because of the flood in a way create a notifier flooding as flood detection. Sensors Ultrasonic sensors measure distance as can be applied as a water level detector, while the sensors used to detect flow velocity of the water flow. Research methods used in the form of literature reviews and making hardware and software using sensors SR04-HC. Applica-tion of fuzzy logic is used as a data processing of the ultrasonic sensor and flow sensor. Fuzzy logic will result in a decision on environmental conditions. With the decision made by fuzzy logic, early warning against the danger of flooding, in the form of an alarm and display the text that indicates a likelihood of flooding, as well as to social media news delivery can be carried out.

Application of fuzzy logic structures in CAD of digital electronics substantially improves quality of design solutions by providing designers with flexibility in formulating goals and selecting tradeoffs. In addition, the following aspects of a design process are positively impacted by application of fuzzy logic: utilization of domain knowledge, interpretation of uncertainties in design data, and adaptation of design algorithms. We successfully applied fuzzy logic structures in conjunction with constructive and iterative algorithms for selecting of design solutions for different stages of the design process. We also introduced fuzzy logic software development tool to be used in CAD applications.

Applications of fuzzy logic in the control of robotic manipulators

A review on the applications of type-2 fuzzy logic in classification and pattern recognition

The paper deals with application of fuzzy logic in model-based rotor speed observer for sensorless induction motor drive. Stability analysis of selected observer is described in the first part. Fuzzy logic is applied to adjust parameters of proportional-integral adaption algorithm in rotor speed adaption mechanism. The third part is description of a laboratory stand with the induction motor drive and load unit that was used for an experimental verification of the speed observer with application of fuzzy logic. Expected properties of sensorless control of induction motor drive are confirmed by experimental results.