Counterpropagation fuzzy–neural network for streamflow reconstruction

This study combines neural networks and fuzzy arithmetic to present a counterpropagation fuzzy–neural network (CFNN) for streamflow reconstruction. The CFNN has a rule‐based control, a modified self‐organizing counterpropagation network, and a fuzzy control predictor. It can generate rules automatically by increasing the training data to improve the accuracy of streamflow reconstruction. The CFNN establishes the input and output relationship of a watershed without set‐up parameters. The parameters are estimated systematically by the approach converging to an optimal solution. One sequence of data generated by the Monte Carlo method is used to demonstrate the accuracy of the CFNN. The streamflow data of the Da‐chia River, in central Taiwan, is also used to evaluate the performances of the CFNN. The results indicate the reliability and accuracy of the CFNN for streamflow reconstruction. Copyright © 2001 John Wiley & Sons, Ltd.     

补偿模糊神经网络在储层参数预测中的应用

为了克服常规BP神经网络法在预测储层参数中出现学习速度慢、无法结合专家知识等不足，我们引入了补偿模糊神经网络。它是一个结合了补偿模糊逻辑和神经网络的混合系统，由面向控制和面向决策的神经元组成，其模糊运算采用动态的、全局优化运算，学习速度快、学习过程稳定，将其用于储层参数预测效果良好。