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161103 ||| eng |
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|a 9783319414386
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| 100 |
1 |
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|a Sgurev, Vassil
|e [editor]
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| 245 |
0 |
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|a Recent Contributions in Intelligent Systems
|h Elektronische Ressource
|c edited by Vassil Sgurev, Ronald R. Yager, Janusz Kacprzyk, Krassimir T. Atanassov
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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| 300 |
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|a X, 390 p. 92 illus., 23 illus. in color
|b online resource
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| 505 |
0 |
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|a Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing -- Generalized Net Representation of Dataflow Process Networks -- Wireless Sensor Positioning ACO Algorithm -- Time Accounting Artificial Neural Networks for Biochemical Process Models -- Periodic Time-varying Observer-based Learning Control of A/F Ratio in Multi-cylinder IC Engines -- Fuzzy T-S Model Based Design of Min-Max Control for Uncertain Nonlinear Systems -- Modeling Parallel Optimization of the Early Stopping Method of Multilayer Perceptron -- Intelligent Controls for Switched Fuzzy Systems: Synthesis via Non-standard Lyapunov Functions -- A New Architecture for an Adaptive Switching Controller Based on Hybrid Multiple T-S Models -- Optimization of Linear Objective Function under min-Probabilistic Sum Fuzzy Linear Equations Constraint -- Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model ParametersGenesis -- Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System -- Generalized Net Model of the Scapulohumeral Rhythm -- Method for Interpretation of Functions of Propositional Logic by Specific Binary Markov Processes -- Generalized Net Models of Academic Promotion and Doctoral Candidature -- Modeling Telehealth Services with Generalized Nets -- State-Space Fuzzy-Neural Predictive Control -- Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests From Two to Hundred Dimensions.-Intuitionistic Fuzzy Sets Generated by Archimedean Metrics and Ultrametrics.-Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Artificial intelligence
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| 700 |
1 |
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|a Yager, Ronald R.
|e [editor]
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| 700 |
1 |
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|a Kacprzyk, Janusz
|e [editor]
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| 700 |
1 |
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|a Atanassov, Krassimir T.
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Studies in Computational Intelligence
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-41438-6
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-41438-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived “intelligent systems”. New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc
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