Quadratic neural unit and its network in validation of process data of steam turbine loop and energetic boiler

Abstract

This paper discusses results and advantages of the application of quadratic neural units and novel quadratic neural network to modeling of real data for purposes of validation of measured data in energetic processes. A feed forward network of quadratic neural units (a class of higher order neural network) with sequential learning is presented. This quadratic network with this learning technique reduces computational time for models with large number of inputs, sustains optimization convexity of a quadratic model, and also displays sufficient nonlinear approximation capability for the real processes. A comparison of performances of the quadratic neural units, quadratic neural networks, and the use of common multilayer feed forward neural networks all trained by Levenberg-Marquard algorithm is discussed.