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| A Direction Optimization Least Mean Square Algorithm |
| Li Xiao-jian Wang Yong Chen Shao-qing Fu Zhi-hao |
| Department of Automation, University of Science and Technology of China, Hefei 230027, China |
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Abstract The update vector of Least Mean Square (LMS) algorithm is an estimation of the gradient vector, thus its convergence rate is limited by the method of steepest descent. Based on the discussion of basic LMS, a direction optimization method of LMS algorithm is proposed in order to get rid of this speed constraint. In the proposed method, the closest update vector to the Newton direction is chosen based on the analysis of the error signal. Based on the method, a Direction Optimization LMS (DOLMS) algorithm is proposed, and it is extended to the variable step-size DOLMS algorithm. The theoretical analysis and the simulation results show that the proposed method has higher speed of convergence and less computational complexity than traditional block LMS algorithm.
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Received: 16 July 2013
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Corresponding Authors:
Wang Yong
E-mail: yongwang@ustc.edu.cn
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2014, Vol. 36 
