【作者】 谢华；

【导师】 董泽；

【作者基本信息】 华北电力大学（河北） ， 控制理论与控制工程， 2006， 硕士

【摘要】 传统的辨识方法如脉冲响应法、最小二乘法等在线性系统中的应用已经相当成熟,但是对于具有明显的非线性特性的对象,用线性模型是无法描述对象的特征的。用BP网络进行辨识容易陷入局部极小点问题,径向基函数网络因为网络权值与输出呈线性关系,所以可采用保证全局收敛的线性化学习方法,并可获得全局最优解,但其应用难点是中心点的选择不易。所以本文针对网络模型和结构的选择问题,研究了小波神经网络在非线性系统辨识中的应用,提出了小波神经网络辨识的一种算法,并使用本文提出的算法训练网络,然后对非线性系统进行了辨识。试验结果表明训练后的小波网络能够达到很好的辨识效果,而且网络规模小,可以作为实际系统的合理化模型。更多还原

【Abstract】 Although the use of traditional identification methods as Impulse response andleast-square procedure are sophisticated, point to the object with obvious nonlinearcharacteristic, it is difficult to describe the characteristic of object. The identification ofobject is easy to trap in local minimum, Radical basic Function network ,because therelation between network and output is linear, so we can adopt the method which assureglobal convergence and get the globally optimal solution, but it is difficult to choose thecenter point .So point to the election of the mode and structure, the article presents aalgorithm of wavelet network, using the algorithm to train the network, after then used tothe practical nonlinear system. Experimental results show that wavelet network aftertraining is able to obtain better result, and the network scope is small, it can be therationalization mode of the practical system.更多还原