Fuzzy Logic Notes (original) (raw)

Fuzzy Logic, Soft Computing and Applications

The theoretical and practical developments of the theory of fuzzy logic and soft computing are surveyed. Specially, we review the history and main milestones of fuzzy logic (in the wide sense), the more recent development of soft computing, and presenting a view of applications: from the most abstract to the most practical ones. It is widely accepted that the main components of Soft Computing are Fuzzy Logic, Probabilistic Reasoning, Neural Computing and Genetic Algorithms. This four constituents share common features and they are considered complementary instead of competitive. The mentioned technologies can be combined in models which exploit their best characteristics. The applications range from the purely theoretical ones, those which develop new lines in abstract mathematics or logic, passing across the areas of multi-media, preference modelling, information retrieval, hybrid intelligent systems, image processing, etc., to practical applications domains such as robotics and manufacturing, actuarial science, nuclear or medical engineering. The basics of fuzzy logic and soft computing are surveyed to a range of applications of these fields ranging from the purely theoretical to the most practical ones.

A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics

Studies in fuzziness and soft computing, 2017

The series "Studies in Fuzziness and Soft Computing" contains publications on various topics in the area of soft computing, which include fuzzy sets, rough sets, neural networks, evolutionary computation, probabilistic and evidential reasoning, multi-valued logic, and related fields. The publications within "Studies in Fuzziness and Soft Computing" are primarily monographs and edited volumes. They cover significant recent developments in the field, both of a foundational and applicable character. An important feature of the series is its short publication time and worldwide distribution. This permits a rapid and broad dissemination of research results.

Soft computing: Fuzzy Models and Applications

2019

The paper attempts to give protection of soft computing in the investigations of the scientist form the institutes of Informatics, Information technologies and Information and Communications Technologies of the Bulgarian Academy of Sciences. Presented is a short list of 60 publications on Soft Computing.

Lectures on Soft Computing and Fuzzy Logic

2001

The use of general descriptive names, regi stered names. trademarks. etc. in this publication does not imply, even in the absence of a specific statement. that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

A critical study of fuzzy logic systems and its applications

"The idea of fuzzy logic is based close to the human reasoning and regular exercises. It presents predicates which are available in nature and like those either enormous or little. This hypothesis copies human brain science in the matter of how a man settles on the choice speedier. Fuzzy logic is a superset of ordinary (Boolean) logic that has been stretched out to deal with the idea of halfway truth - truth esteems between ""totally obvious"" and ""totally false"". It can be actualized in equipment, programming, or a mix of both. It can be incorporated with anything from little, hand-held items to vast modernized process control frameworks. Deepak Sharma | Sohan Garg""A critical study of fuzzy logic systems and its applications"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1 , December 2016, URL: http://www.ijtsrd.com/papers/ijtsrd105.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/105/a-critical-study-of-fuzzy-logic-systems-and-its-applications/deepak-sharma"

Soft computing and its applications

Fuzzy Sets and Systems, 2004

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.