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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
Overall attention for this book and its chapters
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Fuzzy Logic in Artificial Intelligence
Published by
Lecture notes in computer science, January 1993
DOI 10.1007/3-540-56920-0
978-3-54-056920-6, 978-3-54-047782-2

Erich P. Klement, Wolfgang Slany

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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.
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Altmetric has tracked 20,767,801 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,025 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.