In this paper, a continuous wavelet process neural network (CWPNN) model is proposed based on the wavelet theory and process neural network model. The network offers good compromise between robust implementations resulting from the redundancy characteristic of non-orthogonal wavelets, and efficient functional representations that build on the time-frequency localization property of wavelets. Moreover, the network can deal with continuous input signals directly. The corresponding learning algorithm is given and the network is used to solve the problems of aeroengine condition monitoring. The simulation test results indicate that the CWPNN has a faster convergence speed and higher accuracy than the same scale process neural network (PNN) and BP neural network. This provided an effective way for the problems of aeroengine condition monitoring.
A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classification and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically combine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.