Self-constructing Fuzzy Neural Networks with Extended Kalman Filter
... work employing extended Kalman filter (SFNNEKF) self-constructing neural fuzzy inference network, which is designed and developed. The learning algorithm is a modified Takagi-Sugeno-Kang (TSK) type fuzzy based on EKF is simple and effective and is able to system possessing neural network’s learning ...
... work employing extended Kalman filter (SFNNEKF) self-constructing neural fuzzy inference network, which is designed and developed. The learning algorithm is a modified Takagi-Sugeno-Kang (TSK) type fuzzy based on EKF is simple and effective and is able to system possessing neural network’s learning ...
Computational Intelligence and Active Networks
... to stall on the standardization process), protocol bridged (that translates between different revisions/generations of a service as in the active bridge), and most importantly, services themselves: users are free to customize the network infrastructure to fit their needs, when such needs emerge. Thi ...
... to stall on the standardization process), protocol bridged (that translates between different revisions/generations of a service as in the active bridge), and most importantly, services themselves: users are free to customize the network infrastructure to fit their needs, when such needs emerge. Thi ...
English
... according to Kosko (1999), ‘fuzz’ includes statements that are only partially true in order to define vague terms that have entered human language. This way of thinking has generally not been accepted by modern science. A set is a binary structure to which objects belong. An object either belongs to ...
... according to Kosko (1999), ‘fuzz’ includes statements that are only partially true in order to define vague terms that have entered human language. This way of thinking has generally not been accepted by modern science. A set is a binary structure to which objects belong. An object either belongs to ...
Evolving Fuzzy Neural Networks - Algorithms, Applications
... during learning. The nodes representing membership functions (fuzzy label neurons) can be modified during learning. As in FuNN, each input variable is represented here by a group of spatially arranged neurons to represent different fuzzy domain areas of this variable. For example three neurons can b ...
... during learning. The nodes representing membership functions (fuzzy label neurons) can be modified during learning. As in FuNN, each input variable is represented here by a group of spatially arranged neurons to represent different fuzzy domain areas of this variable. For example three neurons can b ...
Prezentacja programu PowerPoint
... The definition of fuzzy set is generalizing the term of classic set, i.e. allowing the determing function (so-called membership function) to obtain values of extremal states of determined set (one or zero {0,1}), as well as intermediate values from this range (interval [0,1]). So, in a fuzzy set we ...
... The definition of fuzzy set is generalizing the term of classic set, i.e. allowing the determing function (so-called membership function) to obtain values of extremal states of determined set (one or zero {0,1}), as well as intermediate values from this range (interval [0,1]). So, in a fuzzy set we ...
NetworkSecurityAITechniques
... and previously defined. This is why your anti-virus software constantly needs to be updated on a regular basis. There are primarily two approaches to detection, 1) Misuse/Signaturebased/Knowledge-based detection, and 2) Anomaly/Behavior detection. Signatures are static definitions of known patterns ...
... and previously defined. This is why your anti-virus software constantly needs to be updated on a regular basis. There are primarily two approaches to detection, 1) Misuse/Signaturebased/Knowledge-based detection, and 2) Anomaly/Behavior detection. Signatures are static definitions of known patterns ...
Hybrid Soft Computing Systems: Where Are We Going
... knowledge about the problem to be solved. In particular, FL allows us to use linguistic variables to model dynamic systems. These variables take fuzzy values that are characterized by a label (a sentence generated from the syntax) and a meaning (a membership function determined by a local semantic p ...
... knowledge about the problem to be solved. In particular, FL allows us to use linguistic variables to model dynamic systems. These variables take fuzzy values that are characterized by a label (a sentence generated from the syntax) and a meaning (a membership function determined by a local semantic p ...
G. Castellano, A.M. Fanelli, P. Plantamura, M.A. Torsello
... a WPS: a knowledge discovery engine and a recommendation engine. In the knowledge discovery engine, Web usage mining techniques are employed in order to extract the knowledge about the user’s preferences. In the recommendation engine, the knowledge extracted through the previous analysis is used to ...
... a WPS: a knowledge discovery engine and a recommendation engine. In the knowledge discovery engine, Web usage mining techniques are employed in order to extract the knowledge about the user’s preferences. In the recommendation engine, the knowledge extracted through the previous analysis is used to ...
Description of Attraction-Repulsion Forces by
... As mentioned in Section 1, only a few of previous works have used fuzzy logic to address the first problem, but theirproposed approaches are not powerful enough to deal with the second problem. It should be mentioned that fuzzy logic is a very good technique for handling deterministic uncertainty. B ...
... As mentioned in Section 1, only a few of previous works have used fuzzy logic to address the first problem, but theirproposed approaches are not powerful enough to deal with the second problem. It should be mentioned that fuzzy logic is a very good technique for handling deterministic uncertainty. B ...
Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and
... and a corresponding class. For example, a simple classification might group students into three groups based on their scores: (1) those students whose scores are above 90 (2) those students whose scores are ...
... and a corresponding class. For example, a simple classification might group students into three groups based on their scores: (1) those students whose scores are above 90 (2) those students whose scores are ...
Intrusion Detection Using Data Mining Along Fuzzy Logic and
... VERY_HIGH. Membership functions have been defined for fuzzy variables representing each of these features of the network being monitored. The fuzzy association algorithm has been applied to mine the correlation among the first four features after applying genetic algorithms technique on these associ ...
... VERY_HIGH. Membership functions have been defined for fuzzy variables representing each of these features of the network being monitored. The fuzzy association algorithm has been applied to mine the correlation among the first four features after applying genetic algorithms technique on these associ ...
Soft computing is an association of computing
... • The defuzzification can be performed in several different ways. The most popular method is the centroid method. ...
... • The defuzzification can be performed in several different ways. The most popular method is the centroid method. ...
Artificial Neural Networks.pdf
... whose height is beneath or equal to 3.9 Here “short” is the language descriptor , it applies the same meaning to both x and y but it established that they don’t have a unique definition for short Such type of information associated with dilemma are made feasible to the computers with the tool called ...
... whose height is beneath or equal to 3.9 Here “short” is the language descriptor , it applies the same meaning to both x and y but it established that they don’t have a unique definition for short Such type of information associated with dilemma are made feasible to the computers with the tool called ...
Fuzzy-probabilistic logic for common sense
... At the heart of logical reasoning is the implication operator, often called the “arrow”. In Bayesian networks, nodes represent random variables and links represent probabilistic conditionals of the form P (x|y). Probabilistic conditionals correspond to implications (x ← y) in classical logic3 . P(Z) ...
... At the heart of logical reasoning is the implication operator, often called the “arrow”. In Bayesian networks, nodes represent random variables and links represent probabilistic conditionals of the form P (x|y). Probabilistic conditionals correspond to implications (x ← y) in classical logic3 . P(Z) ...
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.