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Explainable Uncertain Rule-Based Fuzzy Systems, Third Edition

Posted By: AvaxGenius
Explainable Uncertain Rule-Based Fuzzy Systems, Third Edition

Explainable Uncertain Rule-Based Fuzzy Systems, Third Edition by Jerry M. Mendel
English | PDF EPUB (True) | 2024 | 598 Pages | ISBN : 3031353773 | 120.7 MB

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.

Handbook of Abductive Cognition

Posted By: AvaxGenius
Handbook of Abductive Cognition

Handbook of Abductive Cognition by Lorenzo Magnani
English | PDF,EPUB | 2023 | 1921 Pages | ISBN : 3031101340 | 80.2 MB

This Handbook offers the first comprehensive reference guide to the interdisciplinary field of abductive cognition, providing readers with extensive information on the process of reasoning to hypotheses in humans, animals, and in computational machines. It highlights the role of abduction in both theory practice: in generating and testing hypotheses and explanatory functions for various purposes and as an educational device. It merges logical, cognitive, epistemological and philosophical perspectives with more practical needs relating to the application of abduction across various disciplines and practices, such as in diagnosis, creative reasoning, scientific discovery, diagrammatic and ignorance-based cognition, and adversarial strategies.

A Few Things I Know About Her

Posted By: AvaxGenius
A Few Things I Know About Her

A Few Things I Know About Her: A Personally Machine Learning Inspired Approach to Understand Surrounding Nature by Bruno Apolloni
English | EPUB | 2022 | 211 Pages | ISBN : 303094378X | 42.1 MB

This book reconsiders key issues, such as description and explanation, which affect data analytics. For starters: the soul does not exist. Once released from this cumbersome roommate, we are left with complex biological systems: namely, ourselves, who must configure their environment in terms of worlds that are compatible with what they sense. Far from supplying yet another cosmogony, the book provides the cultivated reader with computational tools for describing and understanding data arising from his surroundings, such as climate parameters or stock market trends, even the win/defeat story of his son football team.

Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems

Posted By: AvaxGenius
Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems

Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso
English | EPUB | 2022 | 111 Pages | ISBN : 3030944816 | 14.6 MB

The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.