Springer Handbook of Computational Intelligence

Springer Handbook of Computational Intelligence

Language: English

Pages: 1634

ISBN: 3662435047

Format: PDF / Kindle (mobi) / ePub


The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts:  foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.

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Of the respective fuzzy partition, i. e., the partition-of-unity. It then leads to a simplified version of the inverse F-transform. In later publications [7.15, 16], the Ruspini condition was weakened to obtain an additional degree of freedom and a better approximation by the inverse Ftransform. Definition 7.1 Let x1 < < xn be fixed nodes within Œa; b such that x1 D a; xn D b and n 2. We say that the fuzzy sets A1 ; : : : ; An , identified with their membership functions defined on Œa; b,.

....................................................................... 1361 1363 1365 1371 1373 1374 1374 71 Fundamental Collective Behaviors in Swarm Robotics Vito Trianni, Alexandre Campo ................................................. 71.1 Designing Swarm Behaviours........................................... 71.2 Getting Together: Aggregation ......................................... 71.3 Acting Together: Synchronization ...................................... 71.4 Staying Together:.

S is large and generated by a propositional language, qualitative possibility distributions can be efficiently encoded in possibilistic logic [3.47–49]. A possibilistic logic base K is a set of pairs .pi ; ˛i /, where pi is an expression in classical (propositional or first-order) logic and ˛i > 0 is a element of the value scale L. This pair encodes the constraint N.pi / ˛i where N.pi / is the degree of necessity of the set of models of pi . Each prioritized formula .pi ; ˛i / has a fuzzy set of.

Analysis. In: Fundamentals of Fuzzy Sets, The Handbook of Fuzzy Sets Series, ed. by D. Dubois, H. Prade (Kluwer, Boston 2000) pp. 483– 582 4.78 4.79 4.80 4.81 G. De Cooman, E.E. Kerre: Order norms on bounded partially ordered sets, J. Fuzzy Math. 2, 281–310 (1994) F. Herrera, S. Alonso, F. Chiclana, E. Herrera-Viedma: Computing with words in decision making: Foundations, trends and prospects, Fuzzy Optim. Decis. Mak. 8(4), 337–364 (2009) L.A. Zadeh: Computing with Words – Principal Concepts.

Shm D B˚;ˇ is recovered, see [5.19, 38]. In m general, B˚;ˇ .f / D Shmq .f p / for any f 2 F.X;A/ . m ii) For a strict pseudoaddition ˚ on Œ0; u and a ˚fitting pseudomultiplication ˇ on Œ0; u Œ0; v, see Proposition 5.1, the constraint 1 ˇ 1 Ä 1 means h.b/ Ä 1, and then B˚;ˇ .f / D g 1 Chh.m/ .g.f // , m is obtained as a transformation of the i. e., B˚;ˇ m Choquet integral. For more details, we recommend the original source [5.24], but also [5.25, 39]. Part A | 5.3 Remark 5.2 When considering.

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