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Author: Jorge Casillas Publisher: Springer ISBN: 3540370579 Category : Technology & Engineering Languages : en Pages : 646
Book Description
Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.
Author: Jorge Casillas Publisher: Springer ISBN: 3540370579 Category : Technology & Engineering Languages : en Pages : 646
Book Description
Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.
Author: Tamás Kenesei Publisher: Springer ISBN: 3319219421 Category : Computers Languages : en Pages : 89
Book Description
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.
Author: Krzysztof Cpałka Publisher: Springer ISBN: 3319528815 Category : Technology & Engineering Languages : en Pages : 203
Book Description
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
Author: Michael Margaliot Publisher: World Scientific ISBN: 9789810243340 Category : Technology & Engineering Languages : en Pages : 204
Book Description
Fuzzy logic has found applications in an incredibly wide range of areas in the relatively wide range of areas in the relatively short time since its conception. It was invented by Lotfi Zadeh, a leading systems expert, so it is perhaps not surprising that system theory is one of the areas in which fuzzy logic has made a profound impact. Fuzzy logic combined with the paradigm of computing with words allows the use and manipulation of human knowledge and reasoning in the modeling and control of dynamical systems. This monograph presents new approaches to the construction of fuzzy models and to the design of fuzzy controllers. The emphasis is on developing methods that allow systematic design on the one hand and mathematical analysis of the resulting system on the other. In particular, the methods described allow rigorous analysis of the stability and robustness of the systems, which are crucial issues in control theory. The first theme of the book is a new approach to the system design and analysis of fuzzy controllers, given linguistic information concerning the plant and the control objective. The new approach, fuzzy Lyapunov synthesis, is a computing-with-words version of the well-known (classical) Lyapunov synthesis method. The second theme of the book is to show that fuzzy controllers are in fact solutions to a nonlinear optimal control problem. The authors formulate a novel nonlinear optimal control problem, consisting of a new state-space model -- referred to as the hyperbolic state-space model -- and a new cost functional and show that its solution is a fuzzy controller. This leads to a new framework for fuzzy modeling and control that combines the advantages of the fuzzyworld, such as linguistic interpretability, and of classical optimal control theory, such as guaranteed stability and robustness.
Author: Jorge Casillas Publisher: Springer ISBN: 3540370587 Category : Business & Economics Languages : en Pages : 392
Book Description
Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas is linguistic fuzzy modeling, where the legibility of the obtained model is the main objective. This task is usually developed by means of linguistic (Mamdani) fuzzy rule-based systems. An active research area is oriented towards the use of new techniques and structures to extend the classical, rigid linguistic fuzzy modeling with the main aim of increasing its precision degree. Traditionally, this accuracy improvement has been carried out without considering the corresponding interpretability loss. Currently, new trends have been proposed trying to preserve the linguistic fuzzy model description power during the optimization process. Written by leading experts in the field, this volume collects some representative researcher that pursue this approach.
Author: Jose Maria Alonso Moral Publisher: Springer Nature ISBN: 303071098X Category : Technology & Engineering Languages : en Pages : 232
Book Description
The importance of Trustworthy and Explainable Artificial Intelligence (XAI) is recognized in academia, industry and society. This book introduces tools for dealing with imprecision and uncertainty in XAI applications where explanations are demanded, mainly in natural language. Design of Explainable Fuzzy Systems (EXFS) is rooted in Interpretable Fuzzy Systems, which are thoroughly covered in the book. The idea of interpretability in fuzzy systems, which is grounded on mathematical constraints and assessment functions, is firstly introduced. Then, design methodologies are described. Finally, the book shows with practical examples how to design EXFS from interpretable fuzzy systems and natural language generation. This approach is supported by open source software. The book is intended for researchers, students and practitioners who wish to explore EXFS from theoretical and practical viewpoints. The breadth of coverage will inspire novel applications and scientific advancements.
Author: Tianhua Chen Publisher: ISBN: Category : Fuzzy logic Languages : en Pages : 0
Book Description
Knowledge discovery from data with fuzzy modelling is currently an active research area in the field of computational intelligence. Fuzzy modelling describes systems by establishing relationships between input and output variables with fuzzy logic and fuzzy set theory. One of the main advantages of fuzzy modelling lies in the interpretability, such that they can formulate the knowledge with linguistic fuzzy rules to gain insights into behaviours of a complex system. However, the interpretability is not automatically given due to only using fuzzy rules. Unlike accuracy that can be used to objectively assess performance of the underlying system, interpretability is a subjective property that may be affected by a range of practical issues, especially regarding the representation of the underlying concepts and domain knowledge. Despite of no commonly accepted mechanism to adjudge interpretability, the incorporationof domain expertise encoded as predefined fuzzy sets is desirable to effectivelyinterpret a fuzzy model. This facilitates enhanced transparency in both learning the models and the inferences performed with the learned models.In light of this, the thesis is focused on the automatic generation of accurate andinterpretable fuzzy models expressed as classification rules, where the use of fixed and predefined quantity spaces is a must for semantic interpretability. In this thesis, several approaches are presented with generated fuzzy rules being interpretable, and achieving competitive performance in comparison to state-of-the-art methods. These include: 1) the approach for the acquisition of fuzzy rules with quantifiers following class-dependent simultaneous rule learning strategy; 2) the approach for the acquisition of weighted fuzzy rules where heuristically generated fuzzy rules are initialised, followed by the global search of optimal rule weights; and 3) the approach that works by utilising existing crisp rules generated by a certain crisp rule-based learning classifier, and then performs rule mapping, followed by global genetic rule and condition selection. Furthermore, to enhance the capability of a fuzzy classifier, the thesis also develops a classifier ensemble approach based on the measure of nearest-neighbour-based reliability. Apart from benchmark data sets that have been utilised for systematic experimental verification, the proposed techniquesare applied to a real-world problem of academic journal ranking, demonstrating the efficacy of the present research.
Author: Alfredo Petrosino Publisher: Springer ISBN: 3642237134 Category : Computers Languages : en Pages : 289
Book Description
This book constitutes the refereed proceedings of the 9th International Workshop on Fuzzy Logic and Applications, WILF 2011 held in Trani, Italy in August 2011. The 34 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on advances in theory of fuzzy sets, advances in fuzzy systems, advances in classification and clustering; and applications.
Author: Dan E. Tamir Publisher: Springer ISBN: 3319196839 Category : Technology & Engineering Languages : en Pages : 679
Book Description
This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh’s seminal paper on “fuzzy sets,” published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh’s paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years’ anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments of Fuzzy Logic-based systems.
Author: Jorge Casillas
Author: Jorge Casillas
Author: Tamás Kenesei
Author: Krzysztof Cpałka
Author: Michael Margaliot
Author: Edwin Lughofer
Author: Jorge Casillas
Author: Jose Maria Alonso Moral
Author: Tianhua Chen
Author: Alfredo Petrosino
Author: Dan E. Tamir