纸质出版日期:2019,
网络出版日期:2019-1-25
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提出了一种动态模糊神经网络(D-FNN)算法。在实际应用中,可以用卡尔曼滤波(KF)方法来调节D-FNN结果参数,同时,EKF(扩展卡尔曼滤波)方法用于更新前提参数的中心和宽度,从而使得所有参数都被修正。该算法可用于平滑、滤波或者预测非线性动态系统的状态,同其他基于梯度的在线算法相比,EKF可以加快D-FNN收敛速度。采用总体最小二乘(TLS)方法作为修剪技术来选择D-FNN重要的模糊规则。如果在学习进行时,检测到不活跃的模糊规则并加以剔除,则可获得更为紧凑的DFNN结构, TLS方法是用来补偿线性参数估计问题中数据误差的一种技术。最后针对实际案例进行了仿真分析,验证了该算法的有效性和高效性。
A dynamic fuzzy neural network algorithm (D-FNN) is proposed. In practical application, Kalman filter (KF) is used to adjust the result parameters of D-FNN. Meanwhile, extended Kalman filter (EKF) is used to update the center and width of the premise parameters, so that all parameters can be modified. This algorithm is used to smooth, filter or predict the state of a nonlinear dynamic system. Comparing to other online algorithms which are based on gradient, EKF can accelerate the convergence speed of D-FNN. The total least squares (TLS) is a pruning technique to select the important fuzzy rules of D-FNN, which also is a technique to compensate data error in linear parameter estimation problem. If the inactive fuzzy rules are detected and removed during the learning process, a more compact D-FNN structure can be obtained. Finally, a simulation analysis for actual case verify the effectiveness and efficiency of the algorithm.
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