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Fuzzy Logic in Artificial Intelligence

Overview of attention for book
Cover of 'Fuzzy Logic in Artificial Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 The role of fuzzy logic and soft computing in the conception and design of intelligent systems
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    Chapter 2 A contextual approach for AI systems development
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    Chapter 3 Typicality of concept instances: a semiotic way for its evaluation
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    Chapter 4 Non-conventional conjunctions and implications in fuzzy logic
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    Chapter 5 A comparative fuzzy modal logic
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    Chapter 6 Combining neural networks and fuzzy controllers
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    Chapter 7 A reinforcement learning algorithm based on ‘safety’
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    Chapter 8 GAITS: Fuzzy sets-based algorithms for computing strategies using genetic algorithms
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    Chapter 9 Neural networks and genetic algorithm approaches to auto-design of fuzzy systems
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    Chapter 10 Symbolic and numeric data management in a geographical information system: A fuzzy neural network approach
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    Chapter 11 Approximate reasoning in the modeling of consensus in group decisions
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    Chapter 12 Fuzzy logic-based processing of expert rules used for checking the creditability of small business firms
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    Chapter 13 Fuzzy control in real-time for vision guided autonomous mobile robots
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    Chapter 14 Robot Motion Coordination by Fuzzy control
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    Chapter 15 Fuzzy control schemes for active magnetic bearings
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    Chapter 16 An adaptive fuzzy control module for automatic dialysis
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    Chapter 17 A combination scheme of artificial intelligence and fuzzy pattern recognition in medical diagnosis
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    Chapter 18 Fuzzy concepts for predicting the behaviour of other drivers on a highway
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    Chapter 19 Design of a fuzzy car distance controller
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    Chapter 20 Karl Menger fuzzy logic and artificial intelligence — An experiment in reflection
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    Chapter 21 Workshop for doctoral students in fuzzy-based systems
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    Chapter 22 Industrial and commercial applications of fuzzy logic
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    Chapter 23 Fuzzy scheduling systems
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Title
Fuzzy Logic in Artificial Intelligence
Published by
Lecture notes in computer science, January 1993
DOI 10.1007/3-540-56920-0
ISBNs
978-3-54-056920-6, 978-3-54-047782-2
Editors

Erich P. Klement, Wolfgang Slany

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 February 2017.
All research outputs
#15,484,645
of 24,998,746 outputs
Outputs from Lecture notes in computer science
#4,359
of 8,155 outputs
Outputs of similar age
#53,742
of 66,462 outputs
Outputs of similar age from Lecture notes in computer science
#11
of 16 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,155 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 66,462 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.