8.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。 9.A Finite Element Pressure Gradient Stabilization for the Compressible Navier-Stokes Equations Based on Local Projections;可压缩Navier-Stokes方程的压力梯...
ainput, render it attractive for use in various types of prediction. Although the back propagation (BP) algorithm is commonly used in recent years to perform the training task, some drawbacks are often encountered in the use of this gradient- based method. They include: the training convergence...
M.: The Effect of Adaptive Momentum in Improving the Accuracy of Gradient Descent Back Propagation Algorithm on Classification Problems, J. CCIS Journal of... SMZR Gillani,NM Nawi - 《International Journal of Modern Physics Conference》 被引量: 36发表: 2012年 Improved Back Propagation Algorithm ...
The efficiency of the proposed algorithm is compared with the conventional back propagation gradient descent and the current working back propagation gradient descent with adaptive gain by means of simulation on three benchmark problems namely iris, glass and thyroid....
When the network predicts the results, we use backpropagation to obtain the classification weight, which is the average gradient of the feature maps generated by the last convolution layer. The definition can be represented as follows:(1)wkc=1S∑i∑j∂yc∂Aijkwhere wkc represents the weight...
1.The training process of Back Propagation Neural Network (BPNN) is easily converged at a local minimum, which slows the training process sharply.但在BP网络的训练过程中 ,如何跳出局部极小点是一个难点 ,对此前人已有一些研究成果 ,其中包括改进的梯度下降搜索法 (gradientdescendresearch ,GDR) [5] 、...
Here [Math Processing Error]g is the gradient of the loss function, so we take the -[Math Processing Error]g as the update direction. [Math Processing Error]ϵ can be viewed as a value that regulates the size of the perturbation. Assuming a binary classification scenario, a standard ...
[103]) used gradient and other information to detect key points. This is feasible but can be considered too computationally intensive for use in real-time applications. To accelerate the detection step, Rosten et al.[104]proposed FAST which was one of the first attempts to use machine ...
The method based on Bayesian symbolic regression (BSR) is proposed in [27]—this method belongs to the family of Markov Chain Monte Carlo algorithms (MCMC). Deep Symbolic Regression (DSR), an RNN approach that utilizes the policy gradient search, is proposed in [28]. This mechanism of searc...
and this gradient is back-propagated to hidden layers to dictate an update direction for the weights. An alternative approach is to train the network with layer-wise loss functions. In this paper we demonstrate, for the first time, that layer-wise training can approach the state-of-the-art ...