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1. |
Record Nr. |
UNINA9910349318703321 |
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Titolo |
The Dynamics of Biological Systems / / edited by Arianna Bianchi, Thomas Hillen, Mark A. Lewis, Yingfei Yi |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIV, 267 p. 63 illus., 34 illus. in color.) |
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Collana |
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Mathematics of Planet Earth, , 2524-4264 ; ; 4 |
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Disciplina |
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Soggetti |
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Mathematics |
Biomathematics |
Systems biology |
Biological systems |
Mathematics of Planet Earth |
Mathematical and Computational Biology |
Systems Biology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Chapter1. Dynamical Systems in Biology - A Short Introduction -- Chapter2. Modelling of Molecular Networks -- Chapter3. Large-Scale Epidemic Models and a Graph-Theoretic Method for Constructing Lyapunov Functions -- Chapter4. Mixing in Meta-Population Models -- Chapter5. Structured Population Models for Vector-Borne Infection Dynamics -- Chapter6. Stochastic Population Kinetics and Its Underlying Mathematicothermodynamics -- Chapter7. The Turing Model for Biological Pattern Formation -- Chapter8. Persistence, Competition and Evolution -- Chapter9. Kinetic equations and cell motion: An Introduction. |
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Sommario/riassunto |
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The book presents nine mini-courses from a summer school, Dynamics of Biological Systems, held at the University of Alberta in 2016, as part of the prestigious seminar series: Séminaire de Mathématiques Supérieures (SMS). It includes new and significant contributions in the field of Dynamical Systems and their applications in Biology, Ecology, |
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and Medicine. The chapters of this book cover a wide range of mathematical methods and biological applications. They - explain the process of mathematical modelling of biological systems with many examples, - introduce advanced methods from dynamical systems theory, - present many examples of the use of mathematical modelling to gain biological insight, - discuss innovative methods for the analysis of biological processes, - contain extensive lists of references, which allow interested readers to continue the research on their own. Integrating the theory of dynamical systems with biological modelling, the book will appeal to researchers and graduate students in Applied Mathematics and Life Sciences. . |
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2. |
Record Nr. |
UNINA9910831020703321 |
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Autore |
Mendel Jerry M |
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Titolo |
Explainable Uncertain Rule-Based Fuzzy Systems / / by Jerry M. Mendel |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[3rd ed. 2024.] |
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Descrizione fisica |
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1 online resource (598 pages) |
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Disciplina |
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Soggetti |
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Computational intelligence |
Telecommunication |
Artificial intelligence |
Neural networks (Computer science) |
Computational Intelligence |
Communications Engineering, Networks |
Artificial Intelligence |
Mathematical Models of Cognitive Processes and Neural Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Part 1: Type-1 Fuzzy Sets and Systems -- Short Primers on Type-1 Fuzzy Sets and Fuzzy Logic -- Type-1 Fuzzy Logic Systems -- Part 2: Type-2 Fuzzy Sets -- Sources of Uncertainty -- Type-2 |
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Fuzzy Sets -- Operations on and Properties OF Type-2 Fuzzy Sets -- Type-2 Relations and Compositions -- Centroid of a Type-2 Fuzzy Set: Type-Reduction -- Part 3: Type-2 Fuzzy Logic Systems -- Mamdani Interval Type-2 Fuzzy Logic Systems (IT2 FLSS) -- TSK Interval Type-2 Fuzzy Logic Systems -- General Type-2 Fuzzy Logic Systems (GT2 FLSS) -- Conclusion. |
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Sommario/riassunto |
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The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material. |
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