Nonlinear system diagnosis using neural networks and fuzzy logic

The authors propose a real-time diagnostic system using a combination of neural networks and fuzzy logic. This neuro-fuzzy hybrid system utilizes real-time processing, prediction, and data fusion. A layer of n trained neural networks processes n independent time series (channels) which can be contaminated with environmental noise. Each network is trained to predict the future behavior of one time series. The prediction error and its rate of change from each channel are computed and sent to a fuzzy logic decision output stage, which contains n+1 modules. The (n+1)th final-output module performs data fusion by combining n individual fuzzy decisions that are tuned to match the domain expert's need.<>