Neural Network Modeling and Identification of Dynamical SystemsКНИГИ » ПРОГРАММИНГ
Название: Neural Network Modeling and Identification of Dynamical Systems Автор: Tiumentsev Y., Egorchev M. Издательство: Academic Press Год: 2019 Страниц: 324 Язык: английский Формат: True PDF Размер: 18.1 MB
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.
The traditional approach to mathematical and computer modeling of the dynamical systems does not satisfy this requirement. We can overcome this difficulty by applying techniques of neural network modeling. However, traditional ANN models, which belong to the black box class, do not allow for a complete solution to the task. This circumstance makes it necessary to expand the black box–type neural network models to the gray box class.
The world of controlled dynamical systems is diverse and multifaceted. Among the most important classes of such systems, traditionally difficult to study, are aircraft of various kinds. One of the challenging modern problems of aeronautical engineering is to create highly autonomous robotic unmanned aerial vehicles (UAVs) intended for the accomplishment of civil and military missions under a wide variety of conditions. These missions include patrolling, threat detection, and object protection; monitoring of power lines, pipelines, and forests; aerial photography, monitoring and survey of ice and fishing areas; performing various assembly operations and terrestrial and naval rescue operations; and providing assistance in natural and technogenic disaster recovery, various military operations, and many other scenarios.
- 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
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