The first phase can be described as:First,the network parameters are optimized by the modified ant colony algorithm;second,the parameters optimized in the first phase are further optimized using the steepest descent algorithm in order to get more acute neural parameters.
该算法分两阶段完成,第一阶段用改进的蚁群优化算法进行网络参数优化;第二阶段利用最速下降法对第一阶段得到的网络参数进行进一步的优化,以得到更加精确的网络参数。
参考来源 - 基于蚁群算法的RBF神经网络优化算法·2,447,543篇论文数据，部分数据来源于NoteExpress
Usually, the steepest descent algorithm is used to find the minimum of LOO upper-bound. However, it often gets local optimal solution.
由于该方法易陷入局部最优解，提出了一种基于混合遗传算法求解LOO上界极小点的核参数选择方法。
It is of lower complexity in computation and far more easy to be implemented as compared with the original steepest descent algorithm.
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。
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