Fuzzy Expert Control Systems: Knowledge Base Validation
... By the development of fuzzy expert control systems, the scope of the applications of fuzzy systems in control is enlarged. By them, the advantages of the knowledge-based system, when the integral control of the plant is the ultimate goal, are exploited, but trying to keep under control the time cons ...
... By the development of fuzzy expert control systems, the scope of the applications of fuzzy systems in control is enlarged. By them, the advantages of the knowledge-based system, when the integral control of the plant is the ultimate goal, are exploited, but trying to keep under control the time cons ...
Soft Computing: Constituent and Applications of Soft
... In the soft computing framework, the basic idea which has been developed so far has consisted in supposing that there is a set of resolving agents which are basically algorithms for solving combinatorial optimization problems, and to execute them cooperatively by means of a coordinating agent to sol ...
... In the soft computing framework, the basic idea which has been developed so far has consisted in supposing that there is a set of resolving agents which are basically algorithms for solving combinatorial optimization problems, and to execute them cooperatively by means of a coordinating agent to sol ...
Resume - University of Houston
... bound to pursue the best available course of action. The model allows for feedback information to be input for each cycle of decision making. Equations describing the updated expected utilities in terms of the learning factor are obtained. The estimators obtained from a martingale or a submartingale ...
... bound to pursue the best available course of action. The model allows for feedback information to be input for each cycle of decision making. Equations describing the updated expected utilities in terms of the learning factor are obtained. The estimators obtained from a martingale or a submartingale ...
Survey on Remotely Sensed Image Classification
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
The use of Fuzzy Logic for Artificial Intelligence in Games
... Among other uses, it can be used for NPC decision making such as item or weapon selection, for the control of units’ movement similar to what happens with control systems, for enabling an AI opponent to assess threats and for classification, for example by ranking players and NPCs in terms of health ...
... Among other uses, it can be used for NPC decision making such as item or weapon selection, for the control of units’ movement similar to what happens with control systems, for enabling an AI opponent to assess threats and for classification, for example by ranking players and NPCs in terms of health ...
Artificial Intelligence Winter 2004
... Up to now we have only considered similarities and distances between objects of U, i.e. sim(object, object). There are three more possibilities: 1) sim(membership function, membership function) 2) sim(object, membership function) 3) The third is 2) with permutated arguments (this plays a r ...
... Up to now we have only considered similarities and distances between objects of U, i.e. sim(object, object). There are three more possibilities: 1) sim(membership function, membership function) 2) sim(object, membership function) 3) The third is 2) with permutated arguments (this plays a r ...
Application of intelligent control systems
... traditional control models. Additionally, principles of construction PID controllers and their advantages and disadvantages over fuzzy systems are analyzed. Intelligent control systems and their appliance are revised for further usage in crane control system. Keywords – gantry crane, fuzzy inference ...
... traditional control models. Additionally, principles of construction PID controllers and their advantages and disadvantages over fuzzy systems are analyzed. Intelligent control systems and their appliance are revised for further usage in crane control system. Keywords – gantry crane, fuzzy inference ...
Adaptive Fuzzy Clustering of Data With Gaps
... The clustering problem for multivariate observations often encountered in many applications connected with Data Mining and Exploratory Data Analysis. Conventional approach to solving these problems requires that each observation may belong to only one cluster. There are many situations when a featur ...
... The clustering problem for multivariate observations often encountered in many applications connected with Data Mining and Exploratory Data Analysis. Conventional approach to solving these problems requires that each observation may belong to only one cluster. There are many situations when a featur ...
Does machine learning need fuzzy logic?
... support vector machines [25,1], etc. In general, this means an extension of the representation of corresponding models by means of fuzzy concepts, such as the use of fuzzy instead of crisp partitions in decision tree learning. The effect is an increased flexibility of the model class, which can inde ...
... support vector machines [25,1], etc. In general, this means an extension of the representation of corresponding models by means of fuzzy concepts, such as the use of fuzzy instead of crisp partitions in decision tree learning. The effect is an increased flexibility of the model class, which can inde ...
Introduction to Neuro-fuzzy and Soft computing
... uncertainty and imprecision” [Lotfi A. Zadeh, 1992] SC consists of several computing paradigms including: NN Fuzzy set theory Approximate reasoning Derivative-free optimization methods such as genetic algorithms (GA) & simulated annealing (SA) ...
... uncertainty and imprecision” [Lotfi A. Zadeh, 1992] SC consists of several computing paradigms including: NN Fuzzy set theory Approximate reasoning Derivative-free optimization methods such as genetic algorithms (GA) & simulated annealing (SA) ...
Fuzzy Information Approaches to Equipment Condition Monitoring and Diagnosis
... available at all. Further, DGA results in several pieces of information which may or may not be consistent. For example, a high concentration of one gas may be ignored if other gas concentrations do not indicate a fault developing. Finally, this diagnostic test may be supplemented with other tests, ...
... available at all. Further, DGA results in several pieces of information which may or may not be consistent. For example, a high concentration of one gas may be ignored if other gas concentrations do not indicate a fault developing. Finally, this diagnostic test may be supplemented with other tests, ...
A Partitioned Fuzzy ARTMAP Implementation for Fast Processing of
... train Fuzzy ARTMAP to map every input pattern of the training list to its corresponding output pattern. To achieve the aforementioned goal we present the training list to Fuzzy ARTMAP architecture repeatedly. That is, we present I1 to F1a , O1 to F2b , I2 to F1a , O2 to F2b , and finally IP T to F1a ...
... train Fuzzy ARTMAP to map every input pattern of the training list to its corresponding output pattern. To achieve the aforementioned goal we present the training list to Fuzzy ARTMAP architecture repeatedly. That is, we present I1 to F1a , O1 to F2b , I2 to F1a , O2 to F2b , and finally IP T to F1a ...
a study of intelligent controllers application in distributed systems
... and a fuzzy control. The authors proposed an automatic server allocation approach that uses the end-to-end delay to measure the performance of the predictor in multi-tier server cluster environment. The 95th –percentile end-to-end delay is an important metric to measure in such environments [13]. Th ...
... and a fuzzy control. The authors proposed an automatic server allocation approach that uses the end-to-end delay to measure the performance of the predictor in multi-tier server cluster environment. The 95th –percentile end-to-end delay is an important metric to measure in such environments [13]. Th ...
Week11 - Information Management and Systems
... Thus genetic algorithms should be considered at present more as an instrument for scientific research rather than as a tool for generic practical data analysis, for instance, in finance. ...
... Thus genetic algorithms should be considered at present more as an instrument for scientific research rather than as a tool for generic practical data analysis, for instance, in finance. ...
An introduction to artificial intelligence applications in petroleum
... regression methods. ANNs are relatively insensitive to data noise, as they have the ability to determine the underlying relationship between model inputs and outputs, resulting in good generalization ability. A neural network model can be subjected to additional training in order to adapt itself to ...
... regression methods. ANNs are relatively insensitive to data noise, as they have the ability to determine the underlying relationship between model inputs and outputs, resulting in good generalization ability. A neural network model can be subjected to additional training in order to adapt itself to ...
sv-lncs - ISIS2013
... physiological sensor with accelerometers will help to recognize the user’s activity. In this paper, we use not only an accelerometer but also physiological sensors to help the recognition of activities. Accelerometers have advantages to measure the user’s movement and the physiological signals have ...
... physiological sensor with accelerometers will help to recognize the user’s activity. In this paper, we use not only an accelerometer but also physiological sensors to help the recognition of activities. Accelerometers have advantages to measure the user’s movement and the physiological signals have ...
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.