File Name: static and dynamic neural networks from fundamentals to advanced theory .zip
Robust and Fault-Tolerant Control pp Cite as. This chapter is devoted to the presentation of neural-network models in the context of control systems design.
In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables.
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Robust and Fault-Tolerant Control pp Cite as. This chapter is devoted to the presentation of neural-network models in the context of control systems design. It is divided into four parts. The first two parts introduce the reader to the theory of static and dynamic neural network structures. These parts can be treated as a quick review of already developed and well-documented neural network architectures, giving an insight into their properties and the possibility of their application in control theory.
The third part is focused on the problem of model design. As the majority of control system designs are model based, developing an accurate model of a plant is of crucial importance, especially for nonlinear systems. Two modelling approaches are discussed: forward and inverse modelling. Moreover, the problem of a training of feed-forward and recurrent neural models is described in the context of parallel and series-parallel identification schemes.
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Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory. Author(s): Provides comprehensive treatment of the theory of both static and dynamic neural networks. Summary · PDF · Request permissions Advanced Methods for Learning and Adaptation in MFNNs (Pages: ).
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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Sarevska and N. Sarevska , N. Mastorakis Published Computer Science. This paper considers antenna array synthesis for regular antenna array using neural network. Because of the best approximation property, radial basis function neural network is used.
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