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.