In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a...
In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi...
These lecture notes focus on the synthesis of robust con- trollers for feedback systems, in the presence of unstruc- tured perturbations. It is assumed, as a prerequisites, that the reader is familiar with the basic linear system and au- tomatic control concepts. In part I interpolation theory is used to solve various single-input-single-output (SISO) ro- bust control problems. While the interpolation approach is awkward for multivariable systems, it provides a very natu- ral and simple approach for SISO systems. In particular the interpolation approach requires only elementary knowledge of...
These lecture notes focus on the synthesis of robust con- trollers for feedback systems, in the presence of unstruc- tured perturbations. It is assume...
