摘要
为解决传统机器学习中模型选择、过学习与局部极小值问题,针对自然海底微地形强烈的非线性、不确定性特点,提出海底微地形的LS-SVM预测模型.采用等式约束代替不等式约束,将二次规划问题转化为求解一次线性方程组,提高了收敛速度.实验结果表明,该方法预测结果误差较小,且实时性较好,可满足建立钴结壳最佳切削深度模型的需要.
To solve such problems as model selecting,over-fitting and local minimum,etc,in traditional machine learning,a least square support vector machine（LS-SVM） based forecasting model of seabed micro-topography was put forward due to the strong non-linear and uncertain characteristics of natural micro-topography in the seabed.Equality constraints were used to replace inequality constraints,and quadratic programming problem was transformed to solve linear equations,which improved convergence rate.Tests show that the proposed method has small prediction error and real time properties,which can meet the requirements of the best cutting depth model of cobalt crusts.
出处
《大连海事大学学报》
CAS
CSCD
北大核心
2010年第1期65-68,共4页
Journal of Dalian Maritime University
基金
国家自然科学基金资助项目(50474052)
