Programa Nacional, Tecnologías Informáticas, Maria-amparo Vila Mir et al. · 2013
The main aim of this project is the development of an intelligent system for Data Mining (DM) with the following features:- It must be able to response to the different environments that any user may propose. Specially, it have to deal with imprecise and uncertain data and requirements.- It must be able to provide information about the ”quality ” of the obtained knowledge, by giving alternative techniques to solve the arise problems- It must be capable to learn, by recording the obtained knowledge and using it in new queries. Therefore, it should include deductive abilities. Such a system should include a platform where the different ”classical ” techniques of DM are integrated together, namely those which developed by the project research groups, with a common representation schema and a unified DM language (dmlanguage). Both representation schema, dmlanguage and some DM techniques should be found in the ”Soft Computing ” paradigm.
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Pradip Debnath, Binod Chandra Tripathy · 2024
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2024
The aim of this reprint is to provide a collection of relevant recent research in the field of soft computing, involving works on different methodologies, techniques and algorithms in soft computing, machine learning, computational intelligence, and optimization. This reprint is addressed to scientists and engineers, offering open access to a variety of innovative ideas and developments, which may further motivate new research endeavors and developments in related areas.
Cited by 0Open access
H.-J. Zimmermann · RWTH Publications (RWTH Aachen) · 1997
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Di Nola A, Loia E, GERLA, BRUNELLA · IrInSubria (University of Insubria) · 2009
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Ram, Mangey, Singh, Suraj B. · 2020
Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering.
Cited by 1Open access
Directory of Open access Books (OAPEN Foundation) · 2020
Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering.
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Athanasios V. Vasilakos, Marco Paìnho · Soft Computing · 2003
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PANELLA, Massimo · IRIS Research product catalog (Sapienza University of Rome) · 2012
Il soft computing è un insieme di metodologie di calcolo progettate per definire e risolvere problemi del mondo reale che non sono modellabili matematicamente ovvero sono troppo difficili da modellare attraverso strumenti matematici tradizionali. E’ l’elemento fondamentale della moderna intelligenza computazionale.
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Nissen, Volker · Common Library Network (Der Gemeinsame Bibliotheksverbund) · 2009
Unter dem Begriff Soft Computing werden heute als Kerngebiete Evolutionäre Algorithmen, künstliche Neuronale Netze, die Fuzzy Set Theorie sowie probabilistisches Schließen zusammengefasst. Typisch sind approximative Lösungen ohne analytische Modellierung sowie ein in einem weiten Verständnis naturanaloges Vorgehen.
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2020
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Andrea G. B. Tettamanzi, Marco Tomassini · 2001
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Pradip Debnath, Oscar Castillo, Poom Kumam · 2023
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Zimmermann, H.-J. · RWTH Publications (RWTH Aachen) · 1997
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A. Bonarini, F. Masulli, G.Pasi, Bonarini, Andrea, F., Masulli et al. · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2003
The papers collected in this book are concerned with the application of the so-called "soft-computing" techniques to the aim of defining flexible systems. The topics covered witness the actual research trend towards an integration of distinct formal techniques for defining flexible systems. The contributions in this volume mainly concern the definition of systems in several application fields, such as image processing, control, asset allocation, medicine, time series forecasting, qualitative modeling, support to design, reliability analysis, diagnosis, filtering, data analysis, land mines detection and so forth. The papers presented in this volume are organized into three main thematic sections: Fuzzy Systems, Neural Networks and Genetic and Evolutionary Algorithms, although, as outlined before, some works employ more than one technique from these fields.
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Nadia Nedjah, Luiza de Macedo Mourelle · Studies in computational intelligence · 2013
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Marco P. Schoen · Cambridge University Press eBooks · 2023
Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
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Advances in intelligent systems and computing · 2017
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N. C. Chauhan, M. V. Kartikeyan, Anshul Mittal · Studies in computational intelligence · 2012
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ICSCS 2018 Kollam, Ivan Zelinka, Roman Šenkeřík et al. · Communications in computer and information science · 2018
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