Soft Computing - 123seminarsonly.com
... organs Fuzzy set theory provides a systematic calculus to deal with such information linguistically It performs numerical computation by using linguistic labels stimulated by membership functions It lacks the adaptability to deal with changing external environments ==> incorporate NN learning ...
... organs Fuzzy set theory provides a systematic calculus to deal with such information linguistically It performs numerical computation by using linguistic labels stimulated by membership functions It lacks the adaptability to deal with changing external environments ==> incorporate NN learning ...
Fundamentals of Computational Intelligence
... The course introduces novel concepts in computational intelligence based techniques. It includes concepts on knowledge based reasoning, fuzzy inferencing systems, connectionist modeling based on artificial neural networks, and deep learning. The course material is self-contained but could be used as ...
... The course introduces novel concepts in computational intelligence based techniques. It includes concepts on knowledge based reasoning, fuzzy inferencing systems, connectionist modeling based on artificial neural networks, and deep learning. The course material is self-contained but could be used as ...
NEW APROACHES IN ARTIFICIAL INTELLIGENCE: A GENDERED
... perspective to this new approach of Soft Computing and in particular to one of its early areas: Fuzzy systems. The concept of Fuzzy Set (also Fuzzy Logic) was conceived by Lotfi Zadeh in 1965, and it was defined as a problem-solving and control system methodology which is empirically-based rather th ...
... perspective to this new approach of Soft Computing and in particular to one of its early areas: Fuzzy systems. The concept of Fuzzy Set (also Fuzzy Logic) was conceived by Lotfi Zadeh in 1965, and it was defined as a problem-solving and control system methodology which is empirically-based rather th ...
Extending Fuzzy Description Logics with a Possibilistic Layer
... Nevertheless, it has been widely pointed out that classical ontologies are not appropriate to deal with imprecise, vague and uncertain knowledge, which is inherent to several real-world domains and Semantic Web tasks (e.g. the integration or merging of ontologies). Fuzzy and possibilistic logics hav ...
... Nevertheless, it has been widely pointed out that classical ontologies are not appropriate to deal with imprecise, vague and uncertain knowledge, which is inherent to several real-world domains and Semantic Web tasks (e.g. the integration or merging of ontologies). Fuzzy and possibilistic logics hav ...
Neural Networks and Fuzzy Logic Systems
... JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD IV Year B.Tech. M.E. II-Sem T P C ...
... JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD IV Year B.Tech. M.E. II-Sem T P C ...
Computational Intelligence in R
... Keywords: Computational Intelligence, Artificial Neural Networks, Fuzzy Rule-Based Systems, Evolutionary Computation ...
... Keywords: Computational Intelligence, Artificial Neural Networks, Fuzzy Rule-Based Systems, Evolutionary Computation ...
Intelligent System
... E-mail: [email protected] Website: http://mfyeh.myweb.hinet.net Office: F412B-III Tel: #5518 ...
... E-mail: [email protected] Website: http://mfyeh.myweb.hinet.net Office: F412B-III Tel: #5518 ...
PPT Presentation
... Compositional Rule of Inference 3. Applications Work with Linguistic Variables; modeling with fuzzy if-then rules Fuzzy decision making Fuzzy control © cm ...
... Compositional Rule of Inference 3. Applications Work with Linguistic Variables; modeling with fuzzy if-then rules Fuzzy decision making Fuzzy control © cm ...
10EI212 NEURAL NETWORKS AND FUZZY LOGIC CONTROL
... identification and control of dynamical systems-case studies (Inverted Pendulum, Articulation Control) Unit III Fuzzy Systems Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification – Defuzzification – Fuzzy rules Unit IV: Fuzzy Logic Control Membership function – Knowledge base – Decision-mak ...
... identification and control of dynamical systems-case studies (Inverted Pendulum, Articulation Control) Unit III Fuzzy Systems Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification – Defuzzification – Fuzzy rules Unit IV: Fuzzy Logic Control Membership function – Knowledge base – Decision-mak ...
Artificial Intelligence and Expert Systems (CB711) Lecturer: Dr
... E-mail: [email protected], [email protected] Homepage: http://osp.mans.edu.eg/elbeltagi ...
... E-mail: [email protected], [email protected] Homepage: http://osp.mans.edu.eg/elbeltagi ...
SPRAY PAINTING ROBOT - PG Embedded systems
... AI is a branch of computer science concerned with the study and creation of computer system that exhibit some form of intelligence. The system that learn new concepts and tasks, system that reason and draw useful conclusion about the world around us. ...
... AI is a branch of computer science concerned with the study and creation of computer system that exhibit some form of intelligence. The system that learn new concepts and tasks, system that reason and draw useful conclusion about the world around us. ...
2014 NEURAL NETWORKS AND FUZZY LOGIC CONTROL
... UNIT-II NEURAL NETWORKS FOR CONTROL: Feedback networks-Discrete time hop field networks-Schemes of neuro – control, identification and control of dynamical systems-case studies (Inverted Pendulum, Articulation Control). UNIT-III FUZZY SYSTEMS: Classical sets-Fuzzy, sets-Fuzzy relations-Fuzzification ...
... UNIT-II NEURAL NETWORKS FOR CONTROL: Feedback networks-Discrete time hop field networks-Schemes of neuro – control, identification and control of dynamical systems-case studies (Inverted Pendulum, Articulation Control). UNIT-III FUZZY SYSTEMS: Classical sets-Fuzzy, sets-Fuzzy relations-Fuzzification ...
Some Applications of Fuzzy Logic in Data Mining and Information
... information retrieval, the mining results must be easily understandable by a user in the case of data mining or knowledge discovery. Fuzzy logic provides an interesting tool for such tasks, mainly because of its capability to represent imperfect information, for instance by means of imprecise catego ...
... information retrieval, the mining results must be easily understandable by a user in the case of data mining or knowledge discovery. Fuzzy logic provides an interesting tool for such tasks, mainly because of its capability to represent imperfect information, for instance by means of imprecise catego ...
Type-2 fuzzy sets and systems
Type-2 fuzzy sets and systems generalize Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the very beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of lots of uncertainty. So, what does one do when there is uncertainty about the value of the membership function? The answer to this question was provided in 1975 by the inventor of fuzzy sets, Prof. Lotfi A. Zadeh [27], when he proposed more sophisticated kinds of fuzzy sets, the first of which he called a type-2 fuzzy set. A type-2 fuzzy set lets us incorporate uncertainty about the membership function into fuzzy set theory, and is a way to address the above criticism of type-1 fuzzy sets head-on. And, if there is no uncertainty, then a type-2 fuzzy set reduces to a type-1 fuzzy set, which is analogous to probability reducing to determinism when unpredictability vanishes,.In order to symbolically distinguish between a type-1 fuzzy set and a type-2 fuzzy set, a tilde symbol is put over the symbol for the fuzzy set; so, A denotes a type-1 fuzzy set, whereas à denotes the comparable type-2 fuzzy set. When the latter is done, the resulting type-2 fuzzy set is called a general type-2 fuzzy set (to distinguish it from the special interval type-2 fuzzy set). Prof. Zadeh didn't stop with type-2 fuzzy sets, because in that 1976 paper [27] he also generalized all of this to type-n fuzzy sets. The present article focuses only on type-2 fuzzy sets because they are the next step in the logical progression from type-1 to type-n fuzzy sets, where n = 1, 2, … . Although some researchers are beginning to explore higher than type-2 fuzzy sets, as of early 2009, this work is in its infancy.The membership function of a general type-2 fuzzy set, Ã, is three-dimensional (Fig. 1), where the third dimension is the value of the membership function at each point on its two-dimensional domain that is called its footprint of uncertainty (FOU). For an interval type-2 fuzzy set that third-dimension value is the same (e.g., 1) everywhere, which means that no new information is contained in the third dimension of an interval type-2 fuzzy set. So, for such a set, the third dimension is ignored, and only the FOU is used to describe it. It is for this reason that an interval type-2 fuzzy set is sometimes called a first-order uncertainty fuzzy set model, whereas a general type-2 fuzzy set (with its useful third-dimension) is sometimes referred to as a second-order uncertainty fuzzy set model.The FOU represents the blurring of a type-1 membership function, and is completely described by its two bounding functions (Fig. 2), a lower membership function (LMF) and an upper membership function (UMF), both of which are type-1 fuzzy sets! Consequently, it is possible to use type-1 fuzzy set mathematics to characterize and work with interval type-2 fuzzy sets. This means that engineers and scientists who already know type-1 fuzzy sets will not have to invest a lot of time learning about general type-2 fuzzy set mathematics in order to understand and use interval type-2 fuzzy sets. Work on type-2 fuzzy sets languished during the 1980s and early-to-mid 1990's, although a small number of articles were published about them. People were still trying to figure out what to do with type-1 fuzzy sets, so even though Zadeh proposed type-2 fuzzy sets in 1976, the time was not right for researchers to drop what they were doing with type-1 fuzzy sets to focus on type-2 fuzzy sets. This changed in the latter part of the 1990s as a result of Prof. Jerry Mendel and his student's works on type-2 fuzzy sets and systems (e.g., [12]). Since then, more and more researchers around the world are writing articles about type-2 fuzzy sets and systems.