In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult because the plant is nonlinear and no stationary. Furthermore, it deals with the situation where the plant becomes uncontrollable without any restrictive assumptions. In contrast to previous work  on the same subject, the number of neural networks has been reduced to only one network. The number of the neurons in a network structure as well as choosing certain design parameters was specified a priori. The computer test results have been presented to show performance of proposed neural controller.
The objective of this paper is to present a probabilistic method of analyzing the combinations of snow and wind loads using meteorological data and to determine their combination factors. Calculations are based on data measured at twelve Polish meteorological stations operated by the Institute for Meteorology and Water Management. Data provided are from the years 1966 - 2010. Five combinations of snow load and 10-minute mean wind velocity pressure have been considered. Gumbel probability distribution has been used to fit the empirical distributions of the data. As a result, the interdependence between wind velocity pressure and snow load on the ground for a return period of 50 years has been provided, and the values of the combination factors for snow loads and wind actions are proposed.