A First Study of Fuzzy Cognitive Maps Learning Using Particle
... A few algorithms have been proposed for FCM learning [22, 29]. The main task of the learning procedure is to find a setting of the FCM’s weights, that leads the FCM to a desired steady state. This is achieved through the minimization of a properly defined objective function. Established algorithms a ...
... A few algorithms have been proposed for FCM learning [22, 29]. The main task of the learning procedure is to find a setting of the FCM’s weights, that leads the FCM to a desired steady state. This is achieved through the minimization of a properly defined objective function. Established algorithms a ...
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... Possess the ability to apply AI techniques to solve problems of Game Playing, Expert Systems, Machine Learning and Natural Language Processing UNIT I Introduction: History, Intelligent Systems, Foundations of AI, sub areas of AI, applications. Problem solving – State – Space search and control s ...
... Possess the ability to apply AI techniques to solve problems of Game Playing, Expert Systems, Machine Learning and Natural Language Processing UNIT I Introduction: History, Intelligent Systems, Foundations of AI, sub areas of AI, applications. Problem solving – State – Space search and control s ...
Fuzzy Algorithms for Pattern Recognition in Medical
... in grouping image pixels based on the similarity of their intensity profile in time and the model based on bootstrap methods attempt to select feature weights based on fuzzy methods. Various models for medical diagnosis have been described in the literature [Mangiameli et al. (2004)]. Neural net mod ...
... in grouping image pixels based on the similarity of their intensity profile in time and the model based on bootstrap methods attempt to select feature weights based on fuzzy methods. Various models for medical diagnosis have been described in the literature [Mangiameli et al. (2004)]. Neural net mod ...
Research and Development of Granular Neural Networks
... by Zadeh in the paper “fuzzy sets and information granularity” [9] in 1979, which causes the widespread interest of researchers. Zadeh thinks there is the concept of information granularity in many areas, which just has different manifestations in different areas. Information granularity is prevalen ...
... by Zadeh in the paper “fuzzy sets and information granularity” [9] in 1979, which causes the widespread interest of researchers. Zadeh thinks there is the concept of information granularity in many areas, which just has different manifestations in different areas. Information granularity is prevalen ...
Intelligence decision systems in enterprise information management
... from information systems, such as ERP, SCM, human resource management (HRM), financial management (FM) and CRM. Numerous intelligence decision systems (IDS) have been developed to support decision making for enterprises. A definition of intelligence which is needed to be defined to research in a fie ...
... from information systems, such as ERP, SCM, human resource management (HRM), financial management (FM) and CRM. Numerous intelligence decision systems (IDS) have been developed to support decision making for enterprises. A definition of intelligence which is needed to be defined to research in a fie ...
A Comparison Model for Uncertain Information in
... regarding the hypothesis made. In order to build the certainty-factor systems, the certainty-factor values must be acquired from medical experts. These values are indeed based on the medical expert subjective interpretation without considering any of the probability rules [21]. This approach is more ...
... regarding the hypothesis made. In order to build the certainty-factor systems, the certainty-factor values must be acquired from medical experts. These values are indeed based on the medical expert subjective interpretation without considering any of the probability rules [21]. This approach is more ...
A Fuzzy Ontology Extension of WordNet and EuroWordnet for
... when dealing with longer sets of hyponymic synsets or multiple word senses in each of the synsets. Prototype theory [18] provides an approach to account for such effects of prototypicality on categorization. Prototype theory is based on the fact that concepts are graded. They show different degrees ...
... when dealing with longer sets of hyponymic synsets or multiple word senses in each of the synsets. Prototype theory [18] provides an approach to account for such effects of prototypicality on categorization. Prototype theory is based on the fact that concepts are graded. They show different degrees ...
complete file
... represented in the form of rules that are used to carry out tasks usually performed by human experts [10]. The basis of such rules is the theory of propositional logic which uses propositional variables (true/false) and truth-functional propositional connectives, including conjunction, disjunction, ...
... represented in the form of rules that are used to carry out tasks usually performed by human experts [10]. The basis of such rules is the theory of propositional logic which uses propositional variables (true/false) and truth-functional propositional connectives, including conjunction, disjunction, ...
Towards a theory of Hybrid Intelligent Autonomous Systems
... may not be that every degree of freedom exists in their surrounding environment but the factory robots workplace is challenging and can often contain chaotic ,unpredicted variables. The exact orientation and position of the next object and the required task must be determined. This can vary unpredic ...
... may not be that every degree of freedom exists in their surrounding environment but the factory robots workplace is challenging and can often contain chaotic ,unpredicted variables. The exact orientation and position of the next object and the required task must be determined. This can vary unpredic ...
Artificial Intelligent Application to Power System Protection
... is attached to each neuron and the training enables adjusting of different weights according to the training set. The ANN techniques are attractive because they do not require tedious knowledge acquisition, representation and writing stages and, therefore, can be successfully applied for tasks not f ...
... is attached to each neuron and the training enables adjusting of different weights according to the training set. The ANN techniques are attractive because they do not require tedious knowledge acquisition, representation and writing stages and, therefore, can be successfully applied for tasks not f ...
comparison of purity and entropy of k-means
... distinguishing property of something. External validation is done class labels. Definition of the external validation indices: They are used to measure the extent to which cluster labels affirm with the externally given class labels. The class labels also known as ground truth are taken as base valu ...
... distinguishing property of something. External validation is done class labels. Definition of the external validation indices: They are used to measure the extent to which cluster labels affirm with the externally given class labels. The class labels also known as ground truth are taken as base valu ...
SOM
... • Neural networks for unsupervised learning attempt to discover special patterns from available data without using external help (i.e. RISK FUNCTION). – There is no information about the desired class (or output ) d of an example x. So only x is given. – Self Organising Maps (SOM) are neural network ...
... • Neural networks for unsupervised learning attempt to discover special patterns from available data without using external help (i.e. RISK FUNCTION). – There is no information about the desired class (or output ) d of an example x. So only x is given. – Self Organising Maps (SOM) are neural network ...
Expert system, fuzzy logic, and neural network applications in power
... education and experience over a prolonged period of time. The question is: Is it possible to embed this knowledge in a computer program so that it can replace the human expert? The answer is “yes,” but we need to recognize that human thinking is so complex that no computer program, however sophistic ...
... education and experience over a prolonged period of time. The question is: Is it possible to embed this knowledge in a computer program so that it can replace the human expert? The answer is “yes,” but we need to recognize that human thinking is so complex that no computer program, however sophistic ...
Intelligence
... Expert systems have difficulty in recognising domain boundaries. When given a task different from the typical problems, an expert system might attempt to solve it and fail in rather unpredictable ways. Heuristic rules represent knowledge in abstract form and lack even basic understanding of the doma ...
... Expert systems have difficulty in recognising domain boundaries. When given a task different from the typical problems, an expert system might attempt to solve it and fail in rather unpredictable ways. Heuristic rules represent knowledge in abstract form and lack even basic understanding of the doma ...
Slide - ICT@UP
... Expert systems have difficulty in recognising domain boundaries. When given a task different from the typical problems, an expert system might attempt to solve it and fail in rather unpredictable ways. Heuristic rules represent knowledge in abstract form and lack even basic understanding of the doma ...
... Expert systems have difficulty in recognising domain boundaries. When given a task different from the typical problems, an expert system might attempt to solve it and fail in rather unpredictable ways. Heuristic rules represent knowledge in abstract form and lack even basic understanding of the doma ...
Lecture 1 Introduction to knowledge
... AI was still a relatively new field, academic in nature, with few practical applications apart from playing games. So, to the outsider, the achieved results would be seen as toys, as no AI system at that time could manage realreal-world problems. ...
... AI was still a relatively new field, academic in nature, with few practical applications apart from playing games. So, to the outsider, the achieved results would be seen as toys, as no AI system at that time could manage realreal-world problems. ...
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.