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  • 1
    UID:
    b3kat_BV046451954
    Format: 1 Online-Ressource (XIV, 792 Seiten)
    ISBN: 9783030354459
    Series Statement: Studies in Computational Intelligence 862
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-35444-2
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-35446-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-35447-3
    Language: English
    Subjects: Computer Science
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Melin, Patricia 1962-
    Author information: Kacprzyk, Janusz 1947-
    Author information: Castillo, Oscar 1959-
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  • 2
    UID:
    b3kat_BV046083985
    Format: 1 Online-Ressource (XXII, 1158 Seiten) , Illustrationen
    ISBN: 9783030166571
    Series Statement: Advances in Intelligent Systems and Computing volume 940
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-16656-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-16658-8
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Lernendes System ; Angewandte Informatik
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Melin, Patricia 1962-
    Author information: Abraham, Ajith 1968-
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  • 3
    UID:
    almahu_9949599154802882
    Format: VIII, 103 p. 32 illus., 19 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031477126
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book focuses on the fields of neural networks, swarm optimization algorithms, clustering and fuzzy logic. This book describes a hybrid method with three different techniques of intelligence computation: neural networks, optimization algorithms and fuzzy logic. Within the neural network techniques, competitive neural networks (CNNs) are used, for the optimization algorithms technique, we used the fireworks algorithm (FWA), and in the area of fuzzy logic, the Type-1 Fuzzy Inference Systems (T1FIS) and the Interval Type-2 Fuzzy Inference Systems (IT2FIS) were used, with their variants of Mamdani and Sugeno type, respectively. FWA was adapted for data clustering with the goal to help of competitive neural network to find the optimal number of neurons. It is important to mention that two variants were applied to the FWA: dynamically adjust of parameters with Type-1 Fuzzy Logic (FFWA) as the first one and Interval Type-2 (F2FWA) as the second one. Subsequently, based on the outputs of the CNN and with the goal of classification data, we designed Type-1 and Interval Type-2 Fuzzy Inference Systems of Mamdani and Sugeno type. This book is intended to be a reference for scientists and engineers interested in applying a different metaheuristic or an artificial neural network in order to solve optimization and applied fuzzy logic techniques for solving problems in clustering and classification data. This book is also used as a reference for graduate courses like the following: soft computing, swarm optimization algorithms, clustering data, fuzzy classify and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, new techniques of optimization or to continue the lines of the research proposed by the authors of the book.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031477119
    Additional Edition: Printed edition: ISBN 9783031477133
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    UID:
    almahu_9949435607702882
    Format: X, 252 p. 82 illus., 52 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031220425
    Series Statement: Studies in Computational Intelligence, 1061
    Content: This book covers recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations. In addition, the above-mentioned methods are applied to areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. Nowadays, the main topic of the book is highly relevant, as most current intelligent systems and devices in use utilize some form of intelligent feature to enhance their performance. In addition, on the theoretical side, new and advanced models and algorithms of type-2 and type-3 fuzzy logic are presented, which are of great interest to researchers working on these areas. Also, new nature-inspired optimization algorithms and innovative neural models are put forward in the manuscript, which are very popular subjects, at this moment. There are contributions on theoretical aspects as well as applications, which make the book very appealing to a wide audience, ranging from researchers to professors and graduate students.
    Note: On Decision Making Applications Via Distance Measures -- On Intuitionistic Fuzzy Abstract Algebras -- Generalization of Intuitionistic Fuzzy Submodules of a Module by using Triangular Norms and Conorms and (T, S)-L Subrings -- Fuzzy Dynamic Parameter Adaptation in the Mayfly Algorithm: Implementation of fuzzy adaptation and tests on benchmark functions and Mackey Glass neural network -- Fuzzy classifier using the optimization algorithm by means of particles for the diagnosis of arterial hypertension -- A Survey of Models and Solution Methods for the Internet Shopping Optimization Problem -- A comparison between MFCC and MSE features for text-independent speaker recognition using machine learning algorithms -- Forecasting based on Fuzzy Logic of the Level of Epidemiological Risk for the Mexican State of Tamaulipas -- Bio-Inspired Flower Pollination Algorithm for the Optimization of a Monolithic Neural Network -- Rendezvous and Docking Control of Satellites Using Chaos Synchronization Method with Intuitionistic Fuzzy Sliding Mode Control -- Optimizing a convolutional neural network with a hierarchical genetic algorithm for diabetic retinopathy detection -- Interval Type-3 Fuzzy Systems: a natural evolution from type-1 and type-2 fuzzy systems -- A comparative study between bird swarm algorithm and artificial gorilla troops optimize -- Particle Swarm Optimization of Convolutional Neural Networks for Diabetic Retinopathy Classification.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031220418
    Additional Edition: Printed edition: ISBN 9783031220432
    Additional Edition: Printed edition: ISBN 9783031220449
    Language: English
    Subjects: Computer Science
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 5
    UID:
    almahu_9948130044002882
    Format: XIII, 65 p. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9783030164164
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book presents an extension of the aggregation operator of the generalized interval type-2 Sugeno integral using generalized type-2 fuzzy logic. This extension enables it to handle higher levels of uncertainty when adding any number of sources and types of information in a wide variety of decision-making applications. The authors also demonstrate that the extended aggregation operator offers better performance than other traditional or extended operators. The book is a valuables reference resource for students and researchers working on theory and applications of fuzzy logic in various areas of application where decision making is performed under high levels of uncertainty, such as pattern recognition, time series prediction, intelligent control and manufacturing.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783030164157
    Additional Edition: Printed edition: ISBN 9783030164171
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 6
    UID:
    almahu_9949882721902882
    Format: VIII, 74 p. 21 illus., 20 illus. in color. , online resource.
    Edition: 1st ed. 2024.
    ISBN: 9783031711015
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book provides a new model for clustering, classification, and time series prediction by using artificial neural networks to computationally simulate the behavior of the cognitive functions of the brain is presented. This model focuses on the study of intelligent hybrid neural systems and their use in time series analysis and decision support systems. Therefore, through the development of eight case studies, multiple time series related to the following problems are analyzed: traffic accidents, air quality and multiple global indicators (energy consumption, birth rate, mortality rate, population growth, inflation, unemployment, sustainable development, and quality of life). The main contribution consists of a Generalized Type-2 fuzzy integration of multiple indicators (time series) using both supervised and unsupervised neural networks and a set of Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. The obtained results show the advantages of the proposed model of Generalized Type-2 fuzzy integration of multiple time series attributes. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic techniques for solving problems in classification and prediction. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.
    Note: 1. Introduction to Prediction with Neural Networks -- 2. Literature Review on Prediction with Neural Networks -- 3. Problem Description of Prediction with Neural Networks -- 4. Methodology for Prediction with Neural Networks5 -- Results of Prediction with Neural Networks -- 6. Discussion of Prediction Results with Neural Networks -- 7. Conclusions for Prediction with Neural Networks.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031711008
    Additional Edition: Printed edition: ISBN 9783031711022
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 7
    UID:
    almahu_9949272445702882
    Format: VIII, 102 p. 60 illus., 46 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9783030965150
    Series Statement: Studies in Fuzziness and Soft Computing, 418
    Content: This book briefly reviews the basic concepts of type-2 fuzzy systems and then describes the proposed definitions for interval type-3 fuzzy sets and relations, also interval type-3 inference and systems. The use of type-2 fuzzy systems has become widespread in the leading economy sectors, especially in industrial and application areas, such as services, health, defense, and so on. However, recently the use of interval type-3 fuzzy systems has been receiving increasing attention and some successful applications have been developed in the last year. These issues were taken into consideration for this book, as we did realize that there was a need to offer the main theoretical concepts of type-3 fuzzy logic, as well as methods to design, develop and implement the type-3 fuzzy systems. A review of basic concepts and their use in the design and implementation of interval type-3 fuzzy systems, which are relatively new models of uncertainty and imprecision, are presented. The main focus of this work is based on the basic reasons of the need for interval type-3 fuzzy systems in different areas of application. In addition, we describe methods for designing interval type-3 fuzzy systems and illustrate this with some examples and simulations.
    Note: Chapter 1: Introduction to Interval Type-3 Fuzzy Systems -- Chapter 2: Type-2 Fuzzy Logic Systems -- Chapter 3: Interval Type-3 Fuzzy Sets -- Chapter 4: Interval Type-3 Fuzzy Logic Systems -- Chapter 5: Conclusions of Type-3 Fuzzy Systems.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783030965143
    Additional Edition: Printed edition: ISBN 9783030965167
    Additional Edition: Printed edition: ISBN 9783030965174
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 8
    UID:
    almahu_9949520055002882
    Format: XII, 498 p. 251 illus., 187 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031289996
    Series Statement: Studies in Computational Intelligence, 1096
    Content: In this book, recent theoretical developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, are presented. In addition, the above-mentioned methods are presented in application areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, decision-making, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. There are a group of papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also a group of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical problems. There are also some papers that present theory and practice of neural networks in different application areas. In addition, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas. Finally, there are a group of papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition and classification problems.
    Note: A decision-making approach based on multiple neural networks for clustering and prediction of time series -- Approximation of Physicochemical Properties based on a Mes-sage Passing Neural Network Approach -- Quanvolutional Neural Network applied to MNIST -- Traffic Sign Recognition Using Fuzzy Preprocessing and Deep Neural Networks -- Fuzzy dynamic adaptation of an Artificial Fish Swarm Algo-rithm for the Optimization of Benchmark Functions -- Particle Swarm Optimization Algorithm with Improved Opposi-tion-Based Learning (IOBL-PSO) to Solve Continuous Problems -- Study on the effect of chaotic maps in the formation of new uni-verses in the Multiverse Optimizer algorithm -- Performance comparative of surrogate models as fitness func-tions for metaheuristic algorithms -- A New Continuous Mycorrhiza Optimization Nature-Inspired Algorithm -- Optimal Tuning of an Active Disturbance Rejection Controller using a Particle Swarm Optimization algorithm -- Optimization of Fuzzy Controllers using Distributed Bioinspired Methods with Random parameters -- Application of Compensatory Fuzzy Logic in Diabetes Problem Using Pima-Indians Dataset -- Comparison of the effect of parameter adaptation in bio-inspired CS algorithm using Type-2 Fuzzy Logic -- Interpretability of an Archimedean Compensatory Fuzzy Logic in Data Analytics: some case studies -- A new selection and class prediction using type-1 fuzzy logic ap-plied to a convolutional neural network -- Relaxed Differential Evolution Algorithm -- Automatic Characterization of Time Series Using Metaheuristic Algorithms for Epidemics Spread Analysis -- Comparative study of heuristics for the One-dimensional Bin Pa-cking Problem -- Experimental Evaluation of Adaptive Operators Selection Methods for the Dynamic Multiobjective Evolutionary Algorithm Based on Decomposition (DMOEA/D) -- Automated Machine Learning to improve stock-market forecast-ing using PSO and LSTM networks -- Evolutionary Gaussian-Gradient: A new optimization algorithm for the electromechanical design of gravitational batteries -- A comparison between selection operators heuristics of perturba-tion in CSP -- Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Feedback Control Techniques -- An Evolutionary Bilevel Optimization Approach for Neuroevolu-tion -- Recovering from population extinction in the Animal Life Cycle Algorithm (ALCA) -- Multi-objective optimization through coevolution and outranking methods with uncertainty management -- EDA Experimental Proposal with Mallows Distribution applied to the Mixed No-idle Permutation Flowshop Scheduling problem -- Interval Type-3 Fuzzy Decision Making in Material Surface Quality Control -- Interval Type-3 Fuzzy Decision Making in Quality Evaluation for Speaker Manufacturing.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031289989
    Additional Edition: Printed edition: ISBN 9783031290008
    Additional Edition: Printed edition: ISBN 9783031290015
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Aufsatzsammlung
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 9
    UID:
    almahu_9948674183402882
    Format: IX, 383 p. 175 illus., 117 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9783030687762
    Series Statement: Studies in Computational Intelligence, 940
    Content: We describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also some papers that presents theory and practice of meta-heuristics in different areas of application. Another group of papers describe diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems. .
    Note: Multi-objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters -- Optimization of the Internet Shopping Problem with Shipping Costs -- Multiobjective Algorithms Performance When Solving CEC09 Test Instances -- Analysis of the Efficient Frontier of the Portfolio Selection Problem Instance of the Mexican Capital Market .
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783030687755
    Additional Edition: Printed edition: ISBN 9783030687779
    Additional Edition: Printed edition: ISBN 9783030687786
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 10
    UID:
    almahu_9949226769102882
    Format: VIII, 127 p. 64 illus., 44 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9783030822194
    Series Statement: SpringerBriefs in Computational Intelligence,
    Content: This book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783030822187
    Additional Edition: Printed edition: ISBN 9783030822200
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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