| 036a | XA-DE |
| 037b | eng |
| 077a | 267042698 Buchausg. u.d.T.: ‡Analysis and design of intelligent systems using soft computing techniques |
| 087q | 978-3-540-72431-5 |
| 100b | Melin, Patricia |
| 104b | Castillo, Oscar |
| 108b | Ramírez, Eduardo Gómez |
| 112b | Kacprzyk, Janusz |
| 116b | Pedrycz, Witold |
| 331 | Analysis and Design of Intelligent Systems Using Soft Computing Techniques |
| 410 | Berlin, Heidelberg |
| 412 | Springer-Verlag Berlin Heidelberg |
| 425 | 2007 |
| 425a | 2007 |
| 433 | Online-Ressource (XXI, 855 p. Also available online, digital) |
| 451 | Advances in Soft Computing ; 41 |
| 501 | Includes bibliographical references and index |
| 517 | Front Matter; Fuzzy Logic as the Logic of Natural Languages; A Method for Response Integration in Modular Neural Networks with Type-2 Fuzzy Logic for Biometric Systems; Evolving Type-2 Fuzzy Logic Controllers for Autonomous Mobile Robots; Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment; Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP; An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications; Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox. Evolutionary Computing for Topology Optimization of Type-2 Fuzzy SystemsComparison of the Performance of Seven Classifiers as Effective Decision Support Tools for the Cytodiagnosis of Breast Cancer: A Case Study; MFCM for Nonlinear Blind Channel Equalization; Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification; Density Based Fuzzy Support Vector Machines for Multicategory Pattern Classification; A Modified FCM Algorithm for Fast Segmentation of Brain MR Images; Incorporation of Non-euclidean Distance Metrics into Fuzzy Clustering on Graphics Processing Units. Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative StudyImproved Fuzzy C-Means Segmentation Algorithm for Images with Intensity Inhomogeneity; A Fuzzy-Neural Hierarchical Multi-model for Systems Identification and Direct Adaptive Control; Robust Speed Controller Design Method Based on Fuzzy Control for Torsional Vibration Suppression in Two-Mass System; Self-organizing Fuzzy Controller Based on Fuzzy Neural Network; Decision Making Strategies for Real-Time Train Dispatch and Control; Soft Margin Training for Associative Memories Implemented by Recurrent Neural Networks. Modular Neural Networks with Fuzzy Integration Applied for Time Series ForecastingPredicting Job Completion Time in a Wafer Fab with a Recurrent Hybrid Neural Network; A Hybrid ANN-FIR System for Lot Output Time Prediction and Achievability Evaluation in a Wafer Fab; M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data; Fuzzy Time Series: A Realistic Method to Forecast Gross Domestic Capital of India; Design of Modular Neural Networks with Fuzzy Integration Applied to Time Series Prediction. Characterize the Parameters of Genetic Algorithms Based on Zernike Polynomials for Recovery of the Phase of Interferograms of Closed Fringes Using Hybrid TechniqueRotated Coin Recognition Using Neural Networks; Selected Problems of Intelligent Handwriting Recognition; 3-D Object Recognition Using an Ultrasonic Sensor Array and Neural Networks; Soft System for Road Sign Detection; Nonlinear Neuro-fuzzy Network for Channel Equalization; On the Possibility of Reliably Constructing a Decision Support System for the Cytodiagnosis of Breast Cancer. Spatial Heart Simulation and Analysis Using Unified Neural Network |
| 527 | Buchausg. u.d.T.: ‡Analysis and design of intelligent systems using soft computing techniques |
| 540a | ISBN 978-3-540-72432-2 |
| 700 | |MAT003000 |
| 700 | |*68T05 |
| 700 | |68-06 |
| 700 | |00B25 |
| 700 | |68T40 |
| 700 | |68T27 |
| 700 | |93C85 |
| 700 | |68T10 |
| 700 | |68T20 |
| 700 | |90B35 |
| 700 | |68N19 |
| 700 | |03B52 |
| 700 | |TBJ |
| 700 | |TEC009000 |
| 700b | |006.3/3 |
| 700b | |519 |
| 700b | |004 |
| 700b | |510 |
| 700c | |TA329-348 |
| 700c | |TA640-643 |
| 700g | 1272555577 ST 301 |
| 750 | This book comprises a selection of papers on new methods for analysis and design of hybrid intelligent systems using soft computing techniques from the IFSA 2007 World Congress, held in Cancun, Mexico, June 2007. |
| 753 | This book comprises a selection of papers from IFSA 2007 on new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas. |
| 902s | 209537000 Lernendes System |
| 902s | 212619101 Soft Computing |
| 902f | 00000011 Aufsatzsammlung |
| 907s | 209537000 Lernendes System |
| 907s | 211423319 Fuzzy-Logik |
| 907s | 212619101 Soft Computing |
| 907s | 211423319 Fuzzy-Logik |
| 012 | 27188732X |
| 081 | Analysis and Design of Intelligent Systems Using Soft Computing Techniques |
| 100 | Springer E-Book |
| 125a | Elektronischer Volltext - Campuslizenz |
| 655e | $uhttp://dx.doi.org/10.1007/978-3-540-72432-2 |
