Empirical Functions Learning Samples Beam Dynamics with Delay Recursive Nonlinear Regressive Transformer Generalized Inverse Least Square Method
Institute of Information Theories and Applications FOI ITHEA
The task of approximation-forecasting for a function, represented by empirical data was investigated.
Certain class of the functions as forecasting tools: so called RFT-transformers, – was proposed. Least Square
Method and superposition are the principal composing means for the function generating. Besides, the special
classes of beam dynamics with delay were introduced and investigated to get classical results regarding
gradients. These results were applied to optimize the RFT-transformers. The effectiveness of the forecast was
demonstrated on the empirical data from the Forex market.