Development of Intelligent Monitoring System for Fossil-Fuel Power Plants Using System-Type Neural Networks and Semigroup Theory

摘要

In this paper we focus on an investigation of a mathematical approach to extrapolation, using a combination of a modified neural network architecture and semigroup theory. Semigroup theory provides a unified and powerful tool for the study of differential equations on abstract spaces, covering systems described by ordinary differential equations, partial differential equations, functional differential equations and combinations thereof. The target of this investigation will be the prediction (by way of extrapolation) of the temperatures within the boiler facility of a power plant. Given a set of empirical data with no analytic expression, we first develop an analytic model and then extend that model along a single axis. For applications to control systems, estimation techniques are often required to compensate for an inadequate amount of data, arising from the unavailability of that data.