The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower...
径向基函数神经网络(radial basis function neural network) www.ecice06.com|基于4个网页 2. 辐射基底类神经网路 ...bilistic neural network)和辐射基底类神经网路(radial basis function neural network)。 etds.lib.ncku.edu.tw|基于3个网页 3.
1.A method of predicting surrounding rock displacement is set up by using the RBF nerve network s function of powerful nonlinear mapping and fast convergence rate, in which the monitoring data of surrounding rock displacement are chosen.选择现场监测的围岩位移数据为样本,利用径向基函数RBF神经网络具有...
Sign in to download full-size image Fig. 3.3.Basic architecture of radial basis function neural network. The input layer transforms data to thehidden neuronscontainingradial basisactivation functions. This function generally computes the distance between the network inputs and hidden layer centers...
Radial basis function(RBF) neural network was applied to determine soil types of hilly and mountainous terrains in Fengdu County of the Three Gorges region in China, the elevation in which ranges between 118.5m and 2000m, combining landsat enhanced thematic mapper plus (ETM+) data and topographic...
The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to ...
A radial-basis function neural network model is developed to learn the mapping from quantifiable and nonquantifiable factors describing the work zone traffic control problem to the associated work zone capacity. This model exhibits good generalization properties from a small set of training data, a ...
COMPARATIVE ANALYSIS OF RBF (RADIAL BASIS FUNCTION) NETWORK AND GAUSSIAN FUNCTION IN MULTI-LAYER FEED-FORWARD NEURAL NETWORK (MLFFNN) FOR THE CASE OF FACE RECOGNITIONdoi:10.21474/IJAR01/5597Journal IJARArvind Kumar
To forecast the freight volume more effectively,the authors analyzed the factors influencing freight volume and established an AHP model.Based on this model,a radial basis function neural network model for freight volume forecasting was presented.The historical statistical data from 1985 to 2004 were ...
A reservoir radial-basis function neural network, which is based on the ideas of reservoir computing and neural networks and designated for solving extrapolation tasks of nonlinear non-stationary stochastic and chaotic time series under conditions of a short learning sample, is proposed in the paper....