Complex-Valued Neural Networks Fault Tolerance in Pattern Classification Applications

This paper investigates the fault-tolerance ability of complex-values neural networks (CVNNs) in classification applications. An analysis of the effect of weight loss at the units (neurons) level revealed that the loss of weight in complex neural networks is more critical than in real valued neural networks. A novel weight decay technique for fault tolerance of real-valued neural networks (RVNNs) is proposed and applied to CVNN. The simulation results indicate that the complex-valued neural networks are less fault tolerant than real-valued neural networks. It is also found that while the weight decay technique substantially improves the fault tolerance ability of RVNN, the technique does not necessary improve the fault tolerance of CVNNs.