抽象上理解了后向传播算法,我们就能根据以上算法,实现一个完整的神经网络的后向传播的算法了! # %load network.py """ network.py ~~~ IT WORKS A module to implement the stochastic gradient descent learning algorithm for a feedforward neural network. Gradients are calculated using backpropagation. Note...
A backpropagation algorithm, or backward propagation of errors, is analgorithmthat's used to help trainneural networkmodels. The algorithm adjusts the network's weights to minimize any gaps -- referred to as errors -- between predicted outputs and the actual target output. Weights are adjustable...
Based on the interval order relation and interval possibility degree model, an interval uncertain optimization model can be transformed into a deterministic optimization model, and be solved by some auxiliary optimization algorithm. A numerical example and an engineering application have indicated that ...
backpropagation algorithmhomotopy continuation methodlocal minimumnumerical simulationquasiARX neural network model trainingrecursive procedure... Hu, Jinglu Hu Jinglu,Lu, Xibin Lu Xibin,Hirasawa, K - Training quasi-ARX neural network model by homotopy approach 被引量: 5发表: 2004年 Fractional Analysis ...
eigenvectors for all the positive eigenvalue of Jacobi matrix are calculated as new searching directions.The improved LMBP algorithm is proved that it can get out of saddle point,and it iterates to minima effectively by an example of comparing with the traditional LMBP algorithm and the improved ...
Beyond its use in deep learning, backpropagation is a powerful computational tool in many other areas, ranging from weather forecasting to analyzing numerical stability – it just goes by different names. In fact, the algorithm has been reinvented at least dozens of times in different fields (see...
MLP-ANNs are trained using the backpropagation (error backpropagation) algorithm. The rules of supervised learning (error corrective learning) are used in this method [32,33,34]. This algorithm involves two passes through the MLP-distinct ANN’s layers: forward and reverse training. A pattern ...
CheeseZH: Stanford University: Machine Learning Ex4:Training Neural Network(Backpropagation Algorithm) 1. Feedforward and cost function; 2.Regularized cost function: 3.Sigmoid gradient The gradient for the sigmoid function can be computed as:
Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, ap
The Back-Propagation (BP) algorithm [48] is widely recognized as a powerful tool for training FNNs. It minimizes the error function using the Steepest Descent (SD) method [15] with constant learning rate η: wk+1 = wk ηg(wk... GD Magoulas,MN Vrahatis - Frontiers of Computational Scie...