AND CHATTOPADHYAY, S., Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone , Int. J. Environ. Sci. Tech., 4 , nr. 1, 2007, p. 141G. Bandyopadhyay, S. Chattopadhyay, "Single hidden layer Artificial ...
1) single hidden layer neural networks 单隐层神经网络 1. A design scheme of an adaptive trajectory linearization control system for an aerospace vehicle was presented by usingsingle hidden layer neural networks(SHLNN). 利用单隐层神经网络的逼近能力在线估计系统中存在的不确定性,神经网络输出用以抵消不...
Security Insights Additional navigation options master 1Branch 0Tags Code Folders and files Name Last commit message Last commit date Latest commit Cannot retrieve latest commit at this time. History 7 Commits Single hidden layer neural network ...
test_layer.cu: Contains functions to test forward and backward propagation of layers. test_neural_network.cu: Contains functions to test the neural network. Makefile: Build configuration Building the Project Clone the repository: git clone https://github.com/Po-V/cuda-SimpleNNet.git cd cuda-Sim...
(1996). Learning in single hidden-layer feedforward network: Backpropagation in a spatial interaction modelling context, Geographical Analysis, 28, pp.38-55.Gopal S, Fischer MM (1996) Learning in single hidden-layer feedforward network: backpropagation in a spatial interaction modeling context. ...
E. On the approximation by single hidden layer feedforward neural networks with fixed weights. Neural networks : the official journal of the International ... NJ Guliyev,VE Ismailov - 《Neural Networks》 被引量: 8发表: 2017年 On the approximation by single hidden layer feedforward neural netwo...
Single-hidden layer feedforward neural network (SLFN), as the simplest type of ANN, has been proved to have the universal approximation capability on any given data set. In recent years, a fast training method for SLFN named extreme learning machine (ELM) [3] has been proposed, which ...
In our experiment, both regularization methods are applied to the single hidden layer neural network with various scales of network complexity. The results show that dropout is more effective than L2-norm for complex networks i.e., containing large numbers of hidden neurons. The results of this ...
The classifier is a single hidden-layer feedforward neural network (SLFN), of which the activation function of the hidden units is ' tansig '. Its parameters are determined by Singular Value Decomposition (SVD). Experimental results show that the Neural-SVD model is simple, has low ...
摘要: A recent paper addresses a certain classification problem, and concludes that classification can be achieved using a single hidden layer neural network. We note here that conclusions along sf miler lines in a more general setting were reached in an earlier paper....