Solar activity has important effect on terrestrial environment in which human population lives. Long-term and short-term periodicities in solar activity had influence on secular climate changes, little ice ages and climatic optima. The article summarizes briefly the results and methods of historical climatology. The text resumes the methods of research of the solar activity variation over the last 1500 years, through physical methods as well as through preserved written sources. The basic mechanisms of the effects of solar activity on terrestrial environment and human population are explained as well as the predictions of the upcoming solar activity. This may indicate that we are currently at the beginning of another long-term solar minimum., Kateřina Podolská., and Obsahuje bibliografii
In this paper, we suggest Evolution Algorithms (EA) for development of neural network topologies to find the optimal solution of some problems. Topologies are modified in feed-forward neural networks and in special cases of recurrent neural networks.
We applied two approaches to the tuning of neural networks. One is classical, using evolution principles only. In the other approach, the adaptation phase (training phase) of the neural network is raade in two steps. In the hrst step we use the genetic algorithm to find better than random starting weights (nearly optimal values), in the second step we use the backpropagation algorithm to finish the adaptation phase. This means that the starting weights for the backpropagation algorithm are not random values, but approximately optimum values. In this context, the fitness of a chromosome (neural network) is a function of its estimated test error (its estimated generalization ability).
Some results obtained by these methods are demonstrated in a prediction of Geo-Magnetic Storms (GMS) and Handwriting Recognition (HWR).