Narrow Results

It is well known that artificial neural nets can be used as approximators of any continous functions to any desired degree and therefore be used e.g. in high-speed, real-time process control. Nevertheless, for a given application and a given network architecture the non-trivial task rests to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation.

In this paper the accuracy of the weights and the number of neurons are seen as general system parameters which determine the maximal output information (i.e. the approximation error) by the absolute amount (network description complexity) and the relative distribution of information contained in the network. A new principle of optimal information distribution is proposed and the conditions for the optimal system parameters are derived.

For two examples, a simple linear approximation of a non-linear, quadratic function and a non-linear approximation of the inverse kinematic transformation used in robot manipulator control, the principle of optimal information distribution gives the the optimal system parameters, i.e. the number of neurons and the different resolutions of the variables.

This paper presents the method of assessment of groundwater quality monitoring network taking into account quantity of information, which can be delivered to the control system. This study was carried out on groundwater monitoring network of post-flotation waste disposal site, called "Zelazny Most". This paper shows the pilot study going to reorganization of existing groundwater quality monitoring network. Farther, different scenarios of verification the existing monitoring network are proposed. The density of monitoring network surrounded the reservoir are also considered. The values of transinformation were used to evaluation the amount of information providing by groundwater quality monitoring network.