Improperly tuned wavelet neural network (WNN) has been shown to exhibit unsatisfactory generalization performance. In this study, the tuning is done by an improved fuzzy C-means algorithm, that utilizes a novel similarity measure. This similarity measure takes the orientation as well as the distance into account. The modified WNN was first applied to a benchmark problem. Performance assessments with other approaches were made subsequently. Next, the feasibility of the proposed WNN in forecasting the chaotic Mackey–Glass time series and a real world application problem, i.e., blood glucose level prediction, were studied. An assessment analysis demonstrated that this presented WNN was superior in terms of prediction accuracy. 
Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems