Complexity Management in Fuzzy Systems: A Rule Base Compression Approach


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The main concept in the field, namely, pre-aggregation functions, have appeared in the literature in , in the context of classification problems, replacing classical aggregation operators used in the fuzzy reasoning mechanism of fuzzy rule-based systems FRBS. The excellent performance provided by pre-aggregation functions in FRBSs have increased the interest of the researchers in their use in other kinds of applications that require some kind aggregation process but the full standard monotonicity may be not required, like image processing and deep learning.

Also, their use in real classification problems are appearing in the literature. Then, the objective of this session is to provide researchers in the field with an opportunity to present their most recent developments in applications and to discuss recent trends in this area, as well as to identify potential application problems of interest for researchers.

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Furthermore, the data are often imprecise and will include both quantitative and qualitative elements. For these reasons it is important to extend traditional decision making processes by adding intuitive reasoning, human subjectivity and imprecision. In the age of Big Data, decision making processes for economy and society have to deal with uncertainty, vagueness, and imprecision. Therefore, the application of fuzzy sets and fuzzy logic becomes a hot topic. Aggregation functions are nowadays a basic tool for any procedure where the fusion of information is required. In recent times, the interest and the work in this field is rapidly growing and have led to a deep study of not only classical aggregation functions such as weighted means, t-norms or t-conorms, but also of others such as those constructed by means of Choquet or Sugeno integrals, copulas, overlap and grouping functions or ignorance functions, among many others, as well as of generalizations of the notion of aggregation function, as it is the case of pre-aggregation functions.


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All these developments have been closely linked to an increasing number of applications in many different topics, from image processing to classification, machine learning or decision making, just to mention a few of them. The aim of this special session is to bring researchers in the field of Aggregation Functions, to exchange their ideas and approaches, to discuss and to present latest results on this field, both from a theoretical and an applied point of view.

Wide range of applications reveals the need for a more flexible modelling of imprecision, uncertainty, vagueness and ignorance.

Glossary of artificial intelligence

The proposed new methods are softer than the traditional theories and techniques because being less rigid they more easily adapt to the actual nature of information. For example, integrating fuzzy sets and probability can lead to more robust and interpretable models and tools in data analysis and data mining which better capture all kinds of the information contained in data. Also, in science and engineering the need to analyze and model the true uncertainty associated with complex systems requires a more sophisticated representation of ignorance than that provided by uninformative Bayesian priors.

The aim of this Special Session is to bring together theoreticians and practitioners working on soft probability, statistics and data analysis for exchanging ideas and discussing new trends that enlarge the statistical and uncertainty modelling traditions, towards a flexible and more specific handling of incomplete or subjective information. The workshop is oriented to the study of structures based on quantum mechanics, in its physical, mathematical, philosophical, applied, and interdisciplinary aspects.

Members of IQSA are particularly encouraged to attend, but participation is not limited to the members of IQSA and all scientists interested in the above topics are welcome. The increasing exposure and quick dissemination of scientific results has facilitated to put in contact fields that have historically worked separately on similar problems. Precisely, aggregation problems have been addressed independently by, for instance, mathematicians and statisticians aggregation of real numbers , computer scientists and bioinformaticians aggregation of strings or political scientists and economists aggregation of rankings.

The field of data aggregation, which is now witnessing a golden age due to the accessibility of large databases in the internet era, must take the challenge of bringing together centuries of impressive work spread over countless scientific fields. In particular, this special session aims at providing a forum for incentivating fruitful discussions on topics of potential interest to the field of data aggregation, both from theory and applications and even from an algorithmic point of view. The Special Session will be focused on theoretical and application aspects of social and networking analysis in general and advantage of fuzzy, Bayesian networks, and related models soft computing techniques in this field in particular.

The objective of this special session is to present original developments that are useful in any application of social networking analysis, going from social network processing techniques to more complex aspects of Opinion Mining and Social Network dynamics understanding, defense against Social Engineering Attacks, Human Reliability analysis.

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Social computing is an important and active field of research using tools and results from fuzzy natural language processing theory, linguistics, fuzzy ontologies, fuzzy graphs, fuzzy time series forecasting, Bayesian belief networks, algebraic Bayesian networks as well as other fields. Nowadays, one can find several approaches to Social Soft Computing. In recent years, fuzzy implication functions have become one of the main research lines of the fuzzy logic community. These logical connectives are the generalization of the classical two-valued implication to the infinite-valued setting.

In addition of modelling fuzzy conditionals, they are also used to perform backward and forward inferences in different fuzzy rules based systems. Moreover, they have proved to be useful not only in fuzzy control and approximate reasoning, but also in many other fields like Multi-Valued Logic, Image Processing, Data Mining, Computing with Words and Rough Sets, among others. Due to this great quantity of applications, fuzzy implication functions have attracted the efforts of many researchers also from the theoretical perspective focusing on problems whose solutions provide important insights from the point of view of their applications.

Therefore, this special session seeks to bring together researchers interested in recent advances in the theory of fuzzy implication functions, concerning, among others, characterizations, representations, generalizations and their relationships with fuzzy negations, triangular norms, uninorms and other fuzzy logic connectives. In the current global Information Technology scenario, voluminous information from sources like webpages, blogs, social networks, or digital libraries among others, is available for processing. For this reason, new Information Retrieval, Decision Making and Recommender Systems more and more powerful are necessary nowadays; and related to this issue, new fields are emerging like Sentiment Analysis.

Traditional systems are incompetent to handle these data, therefore, advanced techniques like proposed by Soft Computing Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.

A Rule Base Compression Approach

Users participation in activities leading to expressing their points of views and comments regarding variety of topics is growing every day. At the same time, many people, companies, and organizations are very interested in knowing what users say. On many occasions, it leads to modifications of talked about items, finding solutions to identified issues, and influencing decision-making.


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Fuzzy F -transforms successfully link various transforms Fourier, Laplace, integral, Wavelet, etc. The general idea is to bring an original model into a special space where succeeding computations are easier. In particular, the F-transform transforms an infinitary object a real function into a finitary one a finite vector. Another specific feature of the F-transform consists in including a fuzzy partition in its formal representation.

The session will be focused on new directions in the development of the F-transform theory. They are based on recent results regarding approximation properties of the direct and inverse F-transform in Lp spaces and some particular Sobolev spaces. In parallel, the lattice-based F-transform, where the approximation is estimated in terms of the lattice order, will be discussed as well.

Fuzzy Logic Projects Using Matlab

In image and signal processing, the F-transform effectively solves problems connected with dimensionality reduction and preprocessing of big data. The aim of this special session is to present recent developments and trends in the theory and applications of the F-transform, including all mentioned above. Beside theoretical aspects, the session will be focused on advanced applications in data analysis including handling big data. We invite contributions that extend traditional ways of data analysis and propose adequate methods for various kinds of data processing including, but not limited to the following topics:.

The Special Session will be focused on theoretical and application aspects of image processing. In particular, the special interest will be paid to modern methods that combine sophisticated conventional, fuzzy and neural network techniques together. The main topics of interest are the following:. Topological dynamics is a well established field of mathematics in which the main object of study is a topological dynamical system, i. This field was established approximately one hundred years ago by G. And in the last decades several natural directions combining studies in topological dynamics and fuzzy mathematics have appeared.

The purpose of this special session is to bring together researchers interested in theory and also possible applications of fuzzy dynamical systems and related topics.

Although the title of this special session is about fuzzy dynamical systems, we also welcome contributions on relevant results from non-fuzzy community, for instance, on dynamical properties of multi-valued, set-valued or iterated dynamical systems IFSs. Mathematical analysis, in the broad sense of the term, includes a very large part of mathematics. It includes the theory of functions of a real variable, differential calculus; integral calculus; approximation theory; the theory of ordinary and partial differential equations, the theory of integral equations.

On the other hand, fuzzy mathematics forms a branch of mathematics related to fuzzy set theory and fuzzy logic.

Fuzzy mathematical analysis appears as one of the natural ways to study the uncertainties in related concepts of mathematical analysis. In the recent years, the concepts of fuzzy metric space, fuzzy integral and derivative, fuzzy initial value problem have been proposed. The goal of this session is to bring together researchers interested in recent advances in fuzzy mathematical analysis and its applications. The relation between fuzzy and interval techniques is well known; e. Uncertainty management in rulebased expert systems.

https://noroi-jusatsu.info/wp-content/2020-06-23/90-espionner-un.php

Alexander Gegov (Author of Rule Based Systems for Big Data)

Fuzzy expert systems. Framebased expert systems. Artificial neural networks. Evolutionary computation. Hybrid intelligent systems. Knowledge engineering and data mining.

Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Complexity Management in Fuzzy Systems: A Rule Base Compression Approach

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