Classification of Faults in Cyber-Physical Systems with Complex-Valued Neural Networks

Abstract

In the contribution at hand, multilayer feedforward neural networks based on multi-valued neurons (MLMVN) are applied on a classification problem in the context of cyber-physical systems. MLMVN are a specific type of complex valued-neural networks. The aim is to apply MLMVN on a benchmark dataset and to classify individual states of a motor (one non-fault state and 10 different fault states). For the multi-class classification problem, an evaluation of selected real-valued and complex-valued feedforward neural networks is considered. One finding is that in terms of accuracy, shallow MLMVN significantly outperform similarly constructed real-valued feedforward neural networks on the benchmark dataset. Thus, the high efficiency of such networks could be an advantage when processing data locally in order to improve robustness, performance, and reduce energy consumption on the system in use.