Artificial Neural Network Approximating Problem Beam Dynamics with Delay Optimization
Institute of Information Theories and Applications FOI ITHEA
Special generalizing for the artificial neural nets: so called RFT – FN – is under discussion in the report.
Such refinement touch upon the constituent elements for the conception of artificial neural network, namely, the
choice of main primary functional elements in the net, the way to connect them(topology) and the structure of the
net as a whole. As to the last, the structure of the functional net proposed is determined dynamically just in the
constructing the net by itself by the special recurrent procedure. The number of newly joining primary functional
elements, the topology of its connecting and tuning of the primary elements is the content of the each recurrent
step. The procedure is terminated under fulfilling “natural” criteria relating residuals for example. The functional
proposed can be used in solving the approximation problem for the functions, represented by its observations, for
classifying and clustering, pattern recognition, etc. Recurrent procedure provide for the versatile optimizing
possibilities: as on the each step of the procedure and wholly: by the choice of the newly joining elements,
topology, by the affine transformations if input and intermediate coordinate as well as by its nonlinear coordinate
wise transformations. All considerations are essentially based, constructively and evidently represented by the
means of the Generalized Inverse.