Neural-Based Nonlinear Device Models for Intermodulation Analysis

A new procedure to learn a nonlinear model together with its derivative parameters using a composite neural network is presented.So far neural networks have never been used to extract large-signal device model accounting for distortion parameters.Applying this method to FET devices leads to nonlinear models for current- voltage functions which allow improved prediction of weak and mildly device nonlinearities in the whole bias region. The resulting models have demonstrated to be suitable for both small-signal and large-signal analyses,including intermodulation distortion prediction

Fast non-recursive extraction of individual harmonics using artificial neural networks

A collaborative work between Northumbria University and University of Peradeniya (Sri Lanka). It presents a novel technique based on Artificial Neural Networks for fast extraction of individual harmonic components. The technique was tested on a real-time hardware platform and results obtained showed that it is significantly faster and less computationally complex than other techniques. The paper complements other publications by the author (see paper 1) on the important area of “Power Quality” of electric power networks. It involves the application of advanced techniques in artificial intelligence to solve power systems problems

Solving Harmonics Elimination Problem in Three-Phase Voltage controlled Inverter using Artificial Neural Networks

A novel concept of application of Artificial Neural Networks (ANN) for generating the optimum switching functions for the voltage and harmonic control of DC-to-AC bridge inverters is presented. In many research, the neural network is trained off line using the desired switching angles given by the classic harmonic elimination strategy to any value of the modulation index. This limits the utilisability and the precision in other modulation index values. In order to avoid this problem, a new training algorithm is developed without using the desired switching angles but it uses the desired solution of the elimination harmonic equation, i.e. first harmonics are equal to zero. Theoretical analysis of the proposed solving algorithm with neural networks is provided, and simulation results are given to show the high performance and technical advantages of the developed modulator