Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications
This paper is focused on weight adaptation stability analysis of static and dynamic neural units for prediction applications. The aim of this paper is to provide verifiable conditions in which the weight system is stable during sample-by-sample adaptation. The paper presents a novel approach toward stability of linear and higher-order neural units. A study of utilization of linear and higher-order neural units with the foundations on stability of the gradient descent algorithm for static and dynamic models is addressed.
Non Linear Fitting Methods for Machine Learning
This manuscript presents an analysis of numerical fitting methods used for solving classification problems as discriminant functions in machine learning. Non linear polynomial, exponential, and trigonometric models are mathematically deduced and discussed. Analysis about their pros and cons, and their mathematical modelling are made on what method to chose for what type of highly non linear multi-dimension problems are more suitable to be solved. In this study only deterministic models with analytic solutions are involved, or parameters calculation by numeric methods, which the complete model can subsequently be treated as a theoretical model. Models deduction are summarised and presented a…