M. A. Partyka

Convergence and Rates of Convergence of Recursive Radial Basis Functions Networks in Function Learning and Classification

In this paper we consider convergence and rates of convergence of the normalized recursive radial basis function networks in function learning and classification when network parameters are learned by the empirical risk minimization.

Identification of Influence of Part Tolerances of 3PWR-SE Pump On Its Total Efficiency Taking Into Consideration Multi-Valued Logic Trees

Abstract This paper presents the way of identifying the influence of the tolerance of model pumps (TYPE 3PWR-SE) construction on the total efficiency. The identification of sensitive control dimensions (Value/Tolerance) of examined pumps has been made by means of the multi-valued logic and inductive decision trees. In order to confirm the obtained results, multiple-valued logic trees have been used in the paper according to algorithms for the minimization of individual logic functions. What is more, a specific monotonicity of values of the most key parameters has been determined for the investigated set of pumps having individual specification numbers.

Learning and Convergence of the Normalized Radial Basis Functions Networks

In the paper we analyze convergence and rates of convergence of the normalized radial basis function networks by relating their \(L_2\) error to the \(L_2\) error of the Wolverton-Wagner regression estimate. The network parameters are learned by minimizing the empirical risk and are applied in function learning and classification.

Inductive Decision Tree Analysis of the Validity Rank of Construction Parameters of Innovative Gear Pump after Tooth Root Undercutting

Abstract The article presents an innovative use of inductive algorithm for generating the decision tree for an analysis of the rank validity parameters of construction and maintenance of the gear pump with undercut tooth. It is preventet an alternative way of generating sets of decisions and determining the hierarchy of decision variables to existing the methods of discrete optimization.