Description
Neural Network Modeling and Identification of Dynamical Systems
Authors: Tiumentsev Yury, Egorchev Mikhail
Language: EnglishSubjects for Neural Network Modeling and Identification of Dynamical...:
Keywords
Adams–Bashforth difference method; Adaptive control; Aircraft aerodynamics; ANN model generation; Behavior analysis; Block-oriented approach; CMA-ES algorithm; Development and maintenance of dynamical systems; Dynamical system design; Ensemble of neural controllers; Environment class; Error function derivative; Euler difference method; Feedforward network; Forecast horizon; Homotopy continuation training method; Hypersonic aircraft; Input–output representation; Interaction; Jacobi matrix; Layered digital dynamic network; Model accuracy; Model design procedure; Model predictive control; Model reference adaptive control; Multilayer neural network; Multimode dynamical system; NARMAX model; NARX model; Neurocontroller; Parameter tuning; Polyharmonic control signal; Short-period angular motion modeling; State space representation; System behavior; Three-axis rotational motion; Tracking error; Uncertainty
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Add to cart the book of Tiumentsev Yury, Egorchev Mikhail332 p. · 19x23.3 cm · Paperback
Description
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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.
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
Mikahil Egorchev is currently a Senior R&D Software Engineer at RoboCV. He is presently working on his Ph.D. in Mathematical Modeling, Numerical Methods and Software Complexes at the Moscow Aviation Institute. He has published 13 articles in his subject areas, which include artificial neural networks, mathematical modeling and computer simulation of nonlinear dynamical systems, numerical optimization methods, and optimal control.
- Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training
- Offers application examples of dynamic neural network technologies, primarily related to aircraft
- Provides an overview of recent achievements and future needs in this area