Modeling Evolutionarily Stable Behavior of Zooplankton Using Pattern Recognition Technologies
Abstract
The problem of finding evolutionarily steady strategy of zooplankton daily vertical migrations is solved by maximizing fitness function. The problem for a class of the simplest data is solved analytically by methods of classical calculus of variations. Piecewise linear and piecewise quadratic approximations of functions of external factors are used. The exact expression of the vertical migration strategy is obtained on the basis of maximizing fitness function for rather simple approximations of input environmental factors. The program complex is created for recognition of qualitative characteristics of evolutionarily steady daily vertical strategy according to approximate data on environment factors (for processing of approximate experimental data generally). Recognition is carried out by means of artificial four-layer neural network. Input data for the program complex is the discrete values set of four external functions, four weight coefficients and also threshold value of vertical movements. Output information of the program complex is the answer concerning existence / lack of the expressed vertical movements of zooplankton for this environment with respect to this threshold value. Training of the network is carried out using the created samples base. The analytical solution of the problem of maximizing fitness function with the simplest approximations of external factors is used for the development of the training data. The training selection contains five types of the entrance functions corresponding to different ways of approximation of external factors. Test date was used for checking network. Test check shows high percent of the right answers of the trained neural network. Comparison of settlement strategy with real observed behavior of a zooplakton is carried out.
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