Sensitivity function multivariable system

The third class of techniques include a frequency-domain method based on the identification of the sensitivity function S s)) and the complementary sensitivity function T s)) from plant data or CPM of multivariable systems [140]. Robust control system design methods seek to maximize closed-loop performance subject to specifications for bandwidth and peak... 

This is a well known result from linear systems theory. The function Sp is called the sensitivity function as it gives the relative change in the input-output sensitivity due the presence of feedback. The index p is here used to denote that it is the sensitivity function of the process itself For the case with multivariable recycle, we get identical results, except that the sensitivity function in this case is a matrix Sp = I — G22 s)Gr s)). ... 

Figure 2 depicts the struetirre of RBF neural networks. RBF networks were introduced into the neural network literature by Broomhead and Lowe (1988). The RBF network model imitates the locally tuned response observed in biological neurons. Neurons with a locally tuned response characteristic can be found in several parts of the nervous system, for example, in cells in the visual cortex sensitive to bars oriented in a certain direction or other visual featirres within a small region of the visual field. These locally tuned neurons show the response characteristics bounded to a small range of the input space. The theoretical basis of the RBF approach lies in the field of interpolation of multivariate functions. The objective of... 

Luminescent lanthanide complexes have been prepared whose emission intensity and lifetime is a sensitive and selective function of pH, p02 and pCl". By appropriate choice of excitation wavelength, the output signal is predictably determined by the local environment and the composition of the mixture of lanthanide complexes chosen. Parallel processing in solution should be possible using multivariate methods of analysis. The benefits of addressing multicomponent systems that are amenable to selective recognition and quantification are discussed. 

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