A Study on Finding the Key Motive of Happiness Using Fuzzy

... virtue. That is happiness as the exercise of virtue. Every human being thinks happiness in his own perspective. Everyone wants to be happy and searching happiness in all their activities. Happiness is typically measured using subjective measures. Happiness cannot be defined in terms of rigid boundar ...

... virtue. That is happiness as the exercise of virtue. Every human being thinks happiness in his own perspective. Everyone wants to be happy and searching happiness in all their activities. Happiness is typically measured using subjective measures. Happiness cannot be defined in terms of rigid boundar ...

An analytic approach for obtaining maximal entropy OWA operator weights ∗ Robert Full´er

... Additionally, Fuller and Majlender (2001) used Lagrange multipliers on Yager’s OWA equation to derive a polynomial equation, which determines the optimal weighting vector under maximal entropy (ME-OWA operator). The proposed approach thus determines the optimal weighting vector under maximal entropy ...

... Additionally, Fuller and Majlender (2001) used Lagrange multipliers on Yager’s OWA equation to derive a polynomial equation, which determines the optimal weighting vector under maximal entropy (ME-OWA operator). The proposed approach thus determines the optimal weighting vector under maximal entropy ...

Special Issue on Semantic Web Meets

... What does one do with so much data? A few years ago there was the issue of data storage. Some of the hottest companies of the time dealt with developing technologies for fast data storage/access. Nowadays, data storage has become extremely cheap. According to Kevin Kelly of Wired magazine, for less ...

... What does one do with so much data? A few years ago there was the issue of data storage. Some of the hottest companies of the time dealt with developing technologies for fast data storage/access. Nowadays, data storage has become extremely cheap. According to Kevin Kelly of Wired magazine, for less ...

Applications of Signatures to Expert Systems Modelling

... logic, neural networks, genetic algorithms and statistic analysis is analysed in [16]. Reliable rule-based systems with uncertainty are obtained in [17] on the basis of semantic data integration. Model-driven engineering techniques for the development of multi-agent systems are proposed in [18]. A c ...

... logic, neural networks, genetic algorithms and statistic analysis is analysed in [16]. Reliable rule-based systems with uncertainty are obtained in [17] on the basis of semantic data integration. Model-driven engineering techniques for the development of multi-agent systems are proposed in [18]. A c ...

X is A - Wiki Index

... In the computational theory of perceptions (Zadeh 1999) the objects of computation are not perceptions per se but their descriptions in a natural language ...

... In the computational theory of perceptions (Zadeh 1999) the objects of computation are not perceptions per se but their descriptions in a natural language ...

Design of A Fuzzy Expert System And A Multi

... worldwide. This kind of disease often leads to fatal outcomes such as heart attack, stroke and renal failure. One of the most dangerous aspects of the hypertension is that you may not know that you have it. In fact, nearly one-third of people who have high blood pressure don't know it. The only way ...

... worldwide. This kind of disease often leads to fatal outcomes such as heart attack, stroke and renal failure. One of the most dangerous aspects of the hypertension is that you may not know that you have it. In fact, nearly one-third of people who have high blood pressure don't know it. The only way ...

Bimal K

... pattern. The network has the capability to "learn" because of the distributed intelligence contributed by the weights. The input-output pattern matching is possible if appropriate weights are selected. In Figure 11-33, there are altogether 25 weights, and by altering these weights, we can get 25 de ...

... pattern. The network has the capability to "learn" because of the distributed intelligence contributed by the weights. The input-output pattern matching is possible if appropriate weights are selected. In Figure 11-33, there are altogether 25 weights, and by altering these weights, we can get 25 de ...

GRANULAR COMPUTING: A NEW PARADIGM IN INFORMATION

... a program module (Yao 2004). Granules at the lowest level are composed of elements or basic particles of the particular model that is used. For instance, the finest granules are words in an article universe. They are formed with basic particles, i.e., letters. They may be considered as singleton gra ...

... a program module (Yao 2004). Granules at the lowest level are composed of elements or basic particles of the particular model that is used. For instance, the finest granules are words in an article universe. They are formed with basic particles, i.e., letters. They may be considered as singleton gra ...

Optimal Placement and Sizing of Capacitor Banks Using Fuzzy

... The ACO algorithms form a class of meta-heuristic to solve NP-hard combinatorial optimization problems. It has been introduced to solve the travelling salesman problem. The basic idea is to imitate the behavior of real ants foraging for food. In fact, the real ants can found the shortest path from a ...

... The ACO algorithms form a class of meta-heuristic to solve NP-hard combinatorial optimization problems. It has been introduced to solve the travelling salesman problem. The basic idea is to imitate the behavior of real ants foraging for food. In fact, the real ants can found the shortest path from a ...

CS437

... n Artificial neural networks have come a long way from the early models of McCulloch and Pitts to an interdisciplinary subject with roots in neuroscience, psychology, mathematics and engineering, and will continue to develop in both theory and practical applications. ...

... n Artificial neural networks have come a long way from the early models of McCulloch and Pitts to an interdisciplinary subject with roots in neuroscience, psychology, mathematics and engineering, and will continue to develop in both theory and practical applications. ...

Intrusion Detection using Fuzzy Clustering and Artificial Neural

... artificiaANN neural network have obtained an average accuracy of 96.71% which can be considered as very successful. The fuzzy logic provides some flexibility to the uncertain nature of detecting intrusions [16]. The hybrid approach in this paper implements fuzzy clustering and ANN. Fuzzy clustering ...

... artificiaANN neural network have obtained an average accuracy of 96.71% which can be considered as very successful. The fuzzy logic provides some flexibility to the uncertain nature of detecting intrusions [16]. The hybrid approach in this paper implements fuzzy clustering and ANN. Fuzzy clustering ...

For Review Only - Portsmouth Research Portal

... that are useful in Assembly Automation for automatically solving problems that would normally require human intelligence. Seven of these tools are reviewed in this paper: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, case-based reasoning and ambientin ...

... that are useful in Assembly Automation for automatically solving problems that would normally require human intelligence. Seven of these tools are reviewed in this paper: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, case-based reasoning and ambientin ...

Full-Text PDF

... Interpretability [1–3] and accuracy [4] are the two important features of a fuzzy system developed for a specific application. The term ‘interpretability’ describes the capability of a model that allows a human being to understand its behavior by inspecting its functioning or its rule base. On the o ...

... Interpretability [1–3] and accuracy [4] are the two important features of a fuzzy system developed for a specific application. The term ‘interpretability’ describes the capability of a model that allows a human being to understand its behavior by inspecting its functioning or its rule base. On the o ...

Hybrid intelligent systems in petroleum reservoir characterization

... machine learning theory that studies the combination of two or more CI techniques to cooperatively work together to form a single functional entity for better performance (Tsakonas and Dounias 2002; Guan et al. 2003). This process of combining the strengths to overcoming the effects of the weaknesse ...

... machine learning theory that studies the combination of two or more CI techniques to cooperatively work together to form a single functional entity for better performance (Tsakonas and Dounias 2002; Guan et al. 2003). This process of combining the strengths to overcoming the effects of the weaknesse ...

Curriculum vitaeWEB_Feb2015 - Técnico Lisboa

... Intelligent Systems (CIS), IDMEC- IST, and an invited assistant professor at Instituto Superior Técnico, University of Lisbon. Susana Vieira received the MSc and PhD degrees both in Mechanical Engineering in 2005 and 2010, respectively, at Instituto Superior Técnico (IST), Technical University of Li ...

... Intelligent Systems (CIS), IDMEC- IST, and an invited assistant professor at Instituto Superior Técnico, University of Lisbon. Susana Vieira received the MSc and PhD degrees both in Mechanical Engineering in 2005 and 2010, respectively, at Instituto Superior Técnico (IST), Technical University of Li ...

# 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 Ã 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.