decisions making in design process – examples of artificial
... has large amount of details, which are not available in the books, but represent s bond between theoretical and practical knowledge. Mixture of the knowledge and mechanism of deduction (heuristic deduction of humans) represents basic power of expert systems. Expert systems are not universal. Their a ...
... has large amount of details, which are not available in the books, but represent s bond between theoretical and practical knowledge. Mixture of the knowledge and mechanism of deduction (heuristic deduction of humans) represents basic power of expert systems. Expert systems are not universal. Their a ...
Journal of Systems and Software:: A Fuzzy Neural Network for
... networks. Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantag ...
... networks. Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantag ...
AAAI Proceedings Template
... classification and generalising; they are also able to predict events in the future on the basis of history [Mena, 2003]. These abilities may be useful for forensics, where they can be used to collect evidence after a crime has been committed. However, ANNs have four algorithms which can be helpful ...
... classification and generalising; they are also able to predict events in the future on the basis of history [Mena, 2003]. These abilities may be useful for forensics, where they can be used to collect evidence after a crime has been committed. However, ANNs have four algorithms which can be helpful ...
Uncertainty Handling for Sensor Location Estimation in Wireless
... complex and nonlinear deterministic systems [20]. A FLS is an inference system that imitates the human thinking and consists of a fuzzifier, some fuzzy IF–THEN rules, a fuzzy inference engine and a defuzzifier. A simple conventional fuzzy if-then rule with multiple inputs and single output can be sh ...
... complex and nonlinear deterministic systems [20]. A FLS is an inference system that imitates the human thinking and consists of a fuzzifier, some fuzzy IF–THEN rules, a fuzzy inference engine and a defuzzifier. A simple conventional fuzzy if-then rule with multiple inputs and single output can be sh ...
Knowledge acquisition and processing: new methods for
... (Gaussian, triangular, trapezoidal, etc.) ? • How to determine parameter values of the membership functions (centers, widths) ? ...
... (Gaussian, triangular, trapezoidal, etc.) ? • How to determine parameter values of the membership functions (centers, widths) ? ...
Fuzzy Genetic Algorithms
... simple inputs–output relations, similar to neural networks function. The input–output relation is described in each rule, nevertheless, the boundary of the rule areas is fuzzy. The system output from one rule area to the next rule area gradually changes. This is the essential idea of fuzzy systems a ...
... simple inputs–output relations, similar to neural networks function. The input–output relation is described in each rule, nevertheless, the boundary of the rule areas is fuzzy. The system output from one rule area to the next rule area gradually changes. This is the essential idea of fuzzy systems a ...
Research Article A Fuzzy Multicriteria Group Decision-Making Method with
... A new entropy measure of interval-valued intuitionistic fuzzy set (IVIFS) is proposed by using cotangent function, which overcomes several limitations in the existing methods for calculating entropy of IVIFS. The efficiency of the new entropy is demonstrated by comparing it with several classical en ...
... A new entropy measure of interval-valued intuitionistic fuzzy set (IVIFS) is proposed by using cotangent function, which overcomes several limitations in the existing methods for calculating entropy of IVIFS. The efficiency of the new entropy is demonstrated by comparing it with several classical en ...
Rule Insertion and Rule Extraction from Evolving Fuzzy
... “yes” class output nodes (for classification tasks only) THEN the probability of pruning node (j) is HIGH. The above pruning rule is fuzzy and it requires that all fuzzy concepts such as OLD, HIGH, etc., are defined in advance. As a partial case, a fixed value can be used, e.g. a node is old if it h ...
... “yes” class output nodes (for classification tasks only) THEN the probability of pruning node (j) is HIGH. The above pruning rule is fuzzy and it requires that all fuzzy concepts such as OLD, HIGH, etc., are defined in advance. As a partial case, a fixed value can be used, e.g. a node is old if it h ...
Computational Intelligence in Data Mining
... http://ajith.softcomputing.net Keywords: KDD, Computational Intelligence, Soft Computing, Fuzzy Classifier System, Rule Base Reduction, Visualization Received: December 20, 2004 ...
... http://ajith.softcomputing.net Keywords: KDD, Computational Intelligence, Soft Computing, Fuzzy Classifier System, Rule Base Reduction, Visualization Received: December 20, 2004 ...
Neural Networks
... not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not. The ...
... not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not. The ...
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727 PP 45-48 www.iosrjournals.org
... some diet specifications and the précised treatment. Using Mamdani inference system, a combination of data mining technique with fuzzy rules was used in [9] to reduce count of attributes for cardiovascular disease diagnosis which covered two steps classification and diagnosis of the disease and rate ...
... some diet specifications and the précised treatment. Using Mamdani inference system, a combination of data mining technique with fuzzy rules was used in [9] to reduce count of attributes for cardiovascular disease diagnosis which covered two steps classification and diagnosis of the disease and rate ...
Fuzzy Logic and Neural Nets
... • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all the rules we partially believe, and take their weighted average ...
... • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all the rules we partially believe, and take their weighted average ...
Survey on Fuzzy Expert System
... Barforush In [4] author proposed fuzzy expert systems for detection and elimination of fuzzy duplicates to clean the data gathered from different sources. Author suggests the sorted neighborhood method (SNM) in which key is created for each tuples such that the duplicates will have similar keys. The ...
... Barforush In [4] author proposed fuzzy expert systems for detection and elimination of fuzzy duplicates to clean the data gathered from different sources. Author suggests the sorted neighborhood method (SNM) in which key is created for each tuples such that the duplicates will have similar keys. The ...
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.