Hybrid Intelligent Systems Analysis and Design /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Castillo, Oscar (Επιμελητής έκδοσης), Melin, Patricia (Επιμελητής έκδοσης), Kacprzyk, Janusz (Επιμελητής έκδοσης), Pedrycz, Witold (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Σειρά: Studies in Fuzziness and Soft Computing, 208
• Θέματα: Computer science.; Computers.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Computer Science.; Theory of Computation.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

 The objective of this edited volume is to offer a general view at the recent conceptual developments of Soft Computing (SC) regarded as a general methodology supporting the design of hybrid systems along with their diversified applications to modeling, simulation and control of non-linear dynamical systems. As of now, SC methodologies embrace neural networks, fuzzy logic, genetic algorithms and chaos theory. Each of these methodologies exhibits well delineated advantages and disadvantages. Interestingly, they have been found useful in solving a broad range of problems. However, many real-world complex problems require a prudent, carefully orchestrated integration of several of these methodologies to fully achieve the required efficiency, accuracy, and interpretability of the solutions. In this edited volume, an overview of SC methodologies, and their applications to modeling, simulation and control, will be given in an introductory paper by the Editors. Then, detailed methods for integrating the different SC methodologies in solving real-world problems will be given in the papers by the other authors in the book. The edited volume will cover a wide spectrum of applications including areas such as: robotic dynamic systems, non-linear plants, manufacturing systems, and time series prediction. 

 Theory -- Hybridization Schemes in Architectures of Computational Intelligence -- ChapBoltzmann Machines Learning Using High Order Decimation -- Evolutionary Optimization of a Wiener Model -- Synchronization of Chaotic Neural Networks: A Generalized Hamiltonian Systems Approach -- Mediative Fuzzy Logic: A Novel Approach for Handling Contradictory Knowledge -- Intelligent Control Applications -- Direct and Indirect Adaptive Neural Control of Nonlinear Systems -- Simple Tuning of Fuzzy Controllers -- From Type-1 to Type-2 Fuzzy Logic Control: A Stability and Robustness Study -- A Comparative Study of Controllers Using Type-2 and Type-1 Fuzzy Logic -- Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Controllers -- Robotic Applications -- Decision Trees and CBR for the Navigation System of a CNN-based Autonomous Robot -- Intelligent Agents in Distributed Fault Tolerant Systems -- Genetic Path Planning with Fuzzy Logic Adaptation for Rovers Traversing Rough Terrain -- Chattering Attenuation Using Linear-in-the-Parameter Neural Nets in Variable Structure Control of Robot Manipulators with Friction -- Tracking Control for a Unicycle Mobile Robot Using a Fuzzy Logic Controller -- Intelligent Control and Planning of Autonomous Algorithms Mobile Robots Using Fuzzy Logic and Genetic -- Pattern Recognition Applications -- The Role of Neural Networks in the Interpretation of Antique Handwritten Documents -- Reasoning Object Recognition Using Fuzzy Inferential -- The Fuzzy Sugeno Integral as a Decision Operator in the Recognition of Images with Modular Neural Networks -- Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: The Case of Human Face and Fingerprint -- Time Series and Diagnosis -- Optimal Training for Associative Memories: Application to Fault Diagnosis in Fossil Electric Power Plants -- Acceleration Output Prediction of Buildings Using a Polynomial Artificial Neural Network -- Time Series Forecasting of Tomato Prices and Processing in Parallel in Mexico Using Modular Neural Networks -- Modular Neural Networks with Fuzzy Sugeno Integration Applied to Time Series Prediction -- On Linguistic Summaries of Time Series Using a Fuzzy Quantifier Based Aggregation via the Sugeno Integral.