Open AccessBook
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
TLDR
This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.Abstract:
Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.read more
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On digital soil mapping
TL;DR: The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.
Journal ArticleDOI
Adaptive Particle Swarm Optimization
TL;DR: An adaptive particle swarm optimization that features better search efficiency than classical particle Swarm optimization (PSO) is presented and can perform a global search over the entire search space with faster convergence speed.
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Blobworld: image segmentation using expectation-maximization and its application to image querying
TL;DR: Results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects are presented.
Book
Fuzzy Modeling for Control
TL;DR: Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view and focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements, and on the design of nonlinear controllers based on fuzzy models.
Journal ArticleDOI
Review: Intrusion detection by machine learning: A review
TL;DR: This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers and discusses current achievements and limitations in developing intrusion detection systems by machine learning.