A new gradient descent algorithm for unconstrained optimization problem is proposed.

提出了一种新的无约束优化下降算法.

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It can be solved by iteration of gradient descent.

它通过梯度下降迭代求得最小值.

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Gradient descent algorithm using momentum BP network training.

采用动量梯度下降算法训练BP网络.

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Based on gradient descent rule, the BP ( Back Propagation ) algorithm is a local optimization algorithm.

bp 算法基于梯度下降原理,是一种局部寻优算法.

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Finally, all of the parameters of the T - S model are determined accurately with gradient descent algorithm.

最后, 通过梯度下降方法,精确确定T -S 模糊模型的所有参数.

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So we can use the convolution of Laplacian operator to compute the gradient descent.

因此,可以利用有向拉普拉斯算子的卷积来计算梯度下降迭代公式.

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