Neural Network Modeling and Identification of Dynamical Systems - Egorchev, Mikhail
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- Livré entre le 8 et le 14 septembre
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Présentation Neural Network Modeling And Identification Of Dynamical Systems Format Broché
- Livre
Résumé : Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft.
Biographie:
Dr. Yury V. Tiumentsev is currently a full professor at Moscow Aviation Institute, teaching in subjects including computer science, computer-aided design, artificial intelligence, artificial neural networks, and soft computing. He is also the Vice President of the Russian Neural Network Society and Vice-Chairman of the Organization and Program Committee of the Annual All-Russia Scientific and Engineering Conference on Neuroinformatics. Dr. Tiumentsev is also a member of the Scientific Committee and a publication reviewer for the International Conference of Artificial Intelligence and Soft Computing (ICAISC), as well as other conference collections such as the International Joint Conference on Neural Networks (IJCNN). His current research subjects include artificial neural networks, adaptive systems, intelligent control, mathematical modeling and computer simulation of complex systems. Dr. Tiumentsev is the author of the Russian-language monograph entitled Neural Network Modeling of Aircraft Motion, and has also written more than 130 articles on his areas of expertise....
Sommaire: 1. The modeling problem for controlled motion of nonlinear dynamical systems2. Neural network approach to the modeling and control of dynamical systems3. Neural network black box (empirical) modeling of nonlinear dynamical systems for the example of aircraft controlled motion4. Neural network semi-empirical models of controlled dynamical systems5. Neural network semi-empirical modeling of aircraft motion
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