Optimization of back propagation algorithm and GAS-assisted ANN models for hot metal desulphurizationAdaptive neural net (ANN) model of hot metal desulphurization is first optimized by various search methods including the golden section search and Davies-Swann-Campey methods. Logarithmic preprocessing of ...
Now, in this back propagation algorithm blog, let’s go ahead and comprehensively understand “Gradient Descent” optimization. Understanding Gradient Descent Gradient descent is by far the most popular optimization strategy used in Machine Learning and Deep Learning at the moment. It is used while ...
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...
To our knowledge, this is the first work to show a Spiking Neural Network implementation of the exact backpropagation algorithm that is fully on-chip without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge...
Back propagation algorithm is an efficient learning algorithm used in ANN. Performance analysis of rotor position estimation of SRM using artificial neural network techniques The back Propagation algorithm can be viewed as an application of optimization method known in statistics as stochastic approximation...
Backpropagation is an algorithm that trains neural networks by adjusting the weights to minimize the error between the predicted and actual outputs. In our neural network, the weights are associated with layers, so we denote the weight connecting the neuron in layer to neuron in layer as . The...
针对城市消费预测问题, 应用一种改进的BP算法,建立了相应的BP网络 模型,并设计了基于BP神经 网络的城市消费预测系统. 互联网 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. 互联网 展开全部...
The goal of this paper is to develop an algorithm using a genetic approach, to simultaneously evolve feed forward artificial neural network architectures and weights with or without incorporating a gradient-descent learning process such as back propagation. The performance of the evolved neural networks...
Coding a neural network that uses back-propagation lends itself nicely to an object-oriented approach. The class definition used for the demo program is listed inFigure 2. Figure 2 Neural Network Class XML class NeuralNetwork { private int numInput; private int numHidden; private int numOutput...
Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China. In orde... L Wei,X Shuo,B ...