新算法与传统的最陡下降算法相比，具有运算量小、容易实现等优点。
It is of lower complexity in computation and far more easy to be implemented as compared with the original steepest descent algorithm.
文中采用最陡下降算法求解该问题，通过计算机仿真，可以看到该方法与原来采用方法具有相似的结果，为工程应用提供了一种简单、实用的方法。
In this paper, the steepest descent arithmetic is used. The computer simulations results show that it is approximate to the common ones. The new method is a viable way in engineering applications.
基于最陡下降方法，推导出了相应的自适应算法。
The corresponding adaptive algorithm was derived based on the steepest descent method.
然后提出了一种新的LMS算法——改进的最陡下降法，该算法正是求最佳权矢量的一个简单而有效的算法。
Then modified steepest descent method which is a new LMS algorithm of adaptive filters is presented , This method is a simple and effective algorithm to solve optimum coefficient .
经模拟计算，它比传统的基于最陡下降方法的误差反传（SDBEP）算法具有更好的收敛性能。
Simulation computations show that it converges faster than the conventional steepest descent backwards error propagation (SDBEP) algorithm.
给出了该自适应网络的结构，在此基础上给出了网络权值的修正算法，即综合最陡下降法和最小二乘法得到的一种混合学习算法。
The structure of ANFIS is proposed. Then a mixed learning arithmetic based on back promulgate and least-square arithmetic is presented to modify the network parameters.
然后系统阐述了基本维纳滤波原理和自适应滤波器的基本结构模型，接着在此基础上结合最陡下降法引出LMS算法。
Then we explain basic theory of wiener filter and basic structure model of adaptive filter, and combine the method of steepest descent to deduce the LMS.
然后系统阐述了基本维纳滤波原理和自适应滤波器的基本结构模型，接着在此基础上结合最陡下降法引出LMS算法。
Then we explain basic theory of wiener filter and basic structure model of adaptive filter, and combine the method of steepest descent to deduce the LMS.
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