4 edition of Simulation, knowledge-based computing, and fuzzy statistics found in the catalog.
Simulation, knowledge-based computing, and fuzzy statistics
C. V. NegoitМ¦aМ†
|Statement||Constantin V. Negoita, Dan Ralescu.|
|Series||Van Nostrand Reinhold electrical/computer science and engineering series|
|Contributions||Ralescu, D. A.|
|LC Classifications||QA76.9.C65 N45 1987|
|The Physical Object|
|Pagination||xi, 158 p. ;|
|Number of Pages||158|
|LC Control Number||87008249|
Computational Modeling and Simulation of Intellect: Current State and Future Perspectives confronts the problem of meaning by fusing together methods specific to different fields and exploring the computational efficiency and scalability of these methods. Researchers, instructors, designers of information and management systems, users of these. Keles A, Kolcak M and Keles A () The adaptive neuro-fuzzy model for forecasting the domestic debt, Knowledge-Based Systems, , (), Online publication date: 1-Dec P Y, Murthy M and Gopal L A fast linear separability test by projection of positive points on subspaces Proceedings of the 24th international conference on Machine.
Advances and Innovations in Systems, Computing Sciences and Software Engineering is a collection of world class paper articles addressing the following topics. Image and Pattern Recognition: Compression, Image processing, Signal Processing Architectures, Signal Processing for Communication, Signal Processing Implementation, Speech Compression, and Video Coding Architectures. A fuzzy concept is a concept of which the boundaries of application can vary considerably according to context or conditions, instead of being fixed once and for all. This means the concept is vague in some way, lacking a fixed, precise meaning, without however being unclear or meaningless altogether. It has a definite meaning, which can be made more precise only through further elaboration.
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. A number of online sellers are already accepting preorders of Reinhard Viertl's new book Fuzzy data and Statistics, seemingly to appear in Dec in Wiley. As the name `Wiley' may suggest, this will be the third time (to the best of my knowledge) that fuzzy material makes it .
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Šešelja B, Stojić D and Tepavčević A () On existence of P-valued fuzzy sets with a given collection of cuts, Fuzzy Sets and Systems,(), Online publication date: 1-Mar Li X and Liu B () On distance between fuzzy variables, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, Simulation, knowledge-based computing, and fuzzy statistics (Van Nostrand Reinhold electrical/computer science and engineering series) [C.
V Negoiță] on *FREE* shipping on qualifying offers. Simulation, Knowledge-Based Computing and Fuzzy Statistics offers the first detailed descriptions of 5/5(1). Additional Physical Format: Online version: Negoiță, C.V. (Constantin Virgil).
Simulation, knowledge-based computing, and fuzzy statistics. New York: Van Nostrand. Simulation, Knowledge-Based Computing and Fuzzy Statistics. Technometrics: Vol. 31, No. 1, pp. Cited by: Simulation Knowledge-Based Computing and Fuzzy Statistics by Constantin V.
Negoita; Anca L. Ralescu and a great selection of related books, art and collectibles available now at Fuzzy Statistics - Ebook written by James J.
Buckley. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Fuzzy Statistics.
This book presents an improved mass spring model to simulate soft tissue deformation for surgery simulation, provides a real-time knowledge-based fuzzy logic model for soft tissue deformation, and determines the parameters of mass spring model using three Brand: Springer International Publishing.
Statistics Book Reviews In this section we present reviews of books on topics related to statistics. Please visit our main book reviews index page for reviews on other topics.
These reviews are offered in Simulation hope knowledge-based computing readers will find them useful, visit often, and tell others. Fuzzy hypothesis tests, fuzzy variance analysis, and fuzzy design of experiments are the examples of fuzzy statistical decision-making techniques. In this chapter, we survey the literature of fuzzy statistics and fuzzy statistical decision-making and present the results by graphical : Cengiz Kahraman, Özgür Kabak.
The purpose of this chapter is to provide a short account of fuzzy set and fuzzy reasoning theory in order to help the unfamiliar reader to study and understand easier the rest of the book. The reader who is familiar with the fuzzy sets can probably find here a ready-to-use material for his(her) by: 3.
Artificial Intelligence Book Reviews In this section we present reviews of books on topics related to artificial intelligence. Please visit our main book reviews index page for reviews on other topics.
These reviews are offered in the hope that readers will find them useful, visit often, and tell others. An Anticipatory Fuzzy Logic Controller Uti-lizing Neural Net Prediction Simulation, The Society for Computer Simulation Expert Systems and Tools: Myths and Realities Apr Abstract.
In this paper, decision influence diagrams are studied when the assessment of utilities with real numerical values is considered to be too restrictive, and the use of fuzzy sets to model the problem in terms of fuzzy utilities seems by: 1.
Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis.
Fuzzy Random Vector and Independence. Simulation, Knowledge-Based Computing and Fuzzy Statistics. Article. Feb ; and integrate fuzzy simulation, neural network, and genetic algorithms.
Order Statistics of Uncertain Random Variables with Application to k-out-of-n System, submitted to Uncertainty, Fuzziness,and Knowledge-Based Systems, (with and ). A Linear Model for Interval-valued Data, submitted (with ).
This book is an anthology of articles that were published in the inaugural volume of the International Journal of Organizational and Collective Intelligence, which provide researchers and practitioners in the communities of computer and information sciences with a forum to advance the practice and understanding of computing theories and empirical analyses as sound technical solutions.
A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values.
Your Paper Your Way We now differentiate between the requirements for new and revised submissions. You may choose to submit your manuscript as a single Word or PDF file to be used in the refereeing process. Only when your paper is at the revision stage, will you be requested to put your paper in to a 'correct format' for acceptance and provide the items required for the publication of your.
applications. In addition the book provides a historical perspective of the development of these concepts in both industry and academia. Negoita, C.V. Simulation, Knowledge-based Computing, and Fuzzy Statistics.
New York, NY. Van Nostrand Reinhold Co. Inc., This text presents the conceptual foundations for the development of Fuzzy by:. Knowledge-based systems are used in solving transportation-planning problems, which often require extensive knowledge bases that need large-scale numerical operations.
However, the multidisciplinary and multiobjective nature of transportation planning systems cannot be analyzed by using only KBSs and necessitates computerized assistance.Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive.
It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.Publisher Summary.
This chapter proposes a new approach to fuzzy adaptive controller design using only system input–output data. The design procedure consists of three steps: First, a fuzzy ARMAX model is identified using the available data; then, a fuzzy controller is derived based on a combination of sliding mode control (SMC) theory and fuzzy control methodology; finally, the controller.