A neural network implementation of dual-frequency output control for multilevel inverters

This paper proposes the use of a multilayer feedforward neural network to control a multilevel inverter-based dual-frequency induction heating power supply, plus the use of distributed computing to train that network. The motivation is because the control function mappings from the desired waveform´s two modulation indices to its step-angles are not simple closed-form expressions, so using look-up tables to implement the mappings accurately and comprehensively would require a significant amount of memory. The neural network was first trained using a single central processing unit, and the results were then compared to similar training using distributed computing (with multiple local area network-connected central processing units). It was found that using distributed computing reduced significantly the training time needed to achieve the desired level of accuracy.