进化小波网络及其在设备状态预测中的应用

结合小波网络和进化计算提出了进化小波网络策略，该策略采用控制级基因和参数级基因分别对网络结构和网络参数进行编码，并将遗传算法与进化规划结合进行进化操作，实现同时对网络结构与网络参数进行进化设计和学习训练。该策略不仅克服了网络训练中的局部极小和不收敛问题，也使网络结构更优，从而提高了网络训练和工作性能。最后分别就函数逼近问题、太阳黑子数预测问题及水轮机组的状态预测问题进行了事例研究，验证了所提出的进化小波网络策略的优越性能和可行性。

EVOLVING WAVELET NEURAL NETWORKS AND ITS APPLICATION IN CONDITION FORECASTING FOR MACHINE

Wavelet neural network is a new type of neural network which combines wavelet theory and BP network and has become a popular tool in approximation and forecasting fields. To improve the performance of the wavelet network, the strategy of evolving wavelet neural networks is proposed, which uses hierarchical chromosome to encode the structure and parameters of the wavelet network, and combines genetic algorithm and evolutionary programming to design and train the network simultaneously through evolution. By means of this strtegy, not only the local minimum problem in training can be overcome and the convergence performance can be improved, but also the structure of the network can be optimized. With respect to function approximation, sunspots time series forecasting and condition forecasting for hydroturbine machine, experimental results are presented to show the superior performance and potential of the proposed hierarchical evolving algorithm based wavelet network.