Retrieval of crop parameters of spinach by radial basis neural network approach using X-band scatterometer data1

2010 ◽  
Vol 36 (4) ◽  
pp. 312-315 ◽  
Author(s):  
Abhishek Pandey ◽  
Sunil Kr. Jha ◽  
R. Prasad
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Radial Basis Neural Network ◽  
X Band ◽  
Radial Basis ◽  
Crop Parameters

Discrimination of Venezuelan spirituous beverages by a trace element-radial basis neural network approach

Talanta ◽  
2008 ◽  
Vol 74 (4) ◽  
pp. 871-878 ◽  
Author(s):  
Edwin A. Hernández-Caraballo ◽  
Rita M. Ávila de Hernández ◽  
Francklin Rivas-Echeverría ◽  
Tarcisio Capote-Luna
Keyword(s):  
Neural Network ◽  
Trace Element ◽  
Network Approach ◽  
Neural Network Approach ◽  
Radial Basis Neural Network ◽  
Radial Basis

scholarly journals Radial Basis Neural Network for Availability Analysis

2019 ◽  
Vol 7 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Deepika Garg ◽  
Naresh Sharma
Keyword(s):  
Neural Network ◽  
Input Pattern ◽  
Network Approach ◽  
Repair Rate ◽  
Neural Network Approach ◽  
Radial Basis Neural Network ◽  
Availability Analysis ◽  
Complete Knowledge ◽  
Radial Basis ◽  
System Functioning

The appliance of radial basis neural network is demostrated in this paper. The method applies failure and repair rate signals to learn the hidden relationship presented into the input pattern. Statistics of availability of several years is considered and collected from the management of concern plant. This data is considered to train and calidate the radial basis neural network (RBNN). Subsequently validated RBNN is used to estimate the availability of concern plant. The main objective of using neural network approach is that it’s not require assumption, nor explicit coding of the problem and also not require the complete knowledge of interdependencies, only requirement is raw data of system functioning.

Accurate radial basis function based neural network approach for analysis of photonic crystal fibers

2008 ◽  
Vol 40 (11-12) ◽  
pp. 891-905 ◽  
Author(s):  
M. F. O. Hameed ◽  
S. S. A. Obayya ◽  
K. Al-Begain ◽  
A. M. Nasr ◽  
M. I. Abo el Maaty
Keyword(s):  
Neural Network ◽  
Photonic Crystal ◽  
Radial Basis Function ◽  
Basis Function ◽  
Photonic Crystal Fibers ◽  
Network Approach ◽  
Neural Network Approach ◽  
Radial Basis ◽  
Crystal Fibers

A Radial Basis Function Neural Network Approach To Determine the Survival of Listeria monocytogenes in Katiki, a Traditional Greek Soft Cheese

2008 ◽  
Vol 71 (4) ◽  
pp. 750-759 ◽  
Author(s):  
EFSTATHIOS Z. PANAGOU
Keyword(s):  
Neural Network ◽  
Listeria Monocytogenes ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Approach ◽  
F Test ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Radial Basis ◽  
Modified Weibull

A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 106 CFU g −1 and stored at 5, 10, 15, and 20°C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5°C, whereas with the neural network all f values were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17°C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary models at both validation temperatures. The results of this work could increase the knowledge basis for the applicability of neural networks as an alternative tool in predictive microbiology.

A Neural Network Approach to Soil Electrical Conductivity Estimation on Earthen Levees Using Spaceborne X-band SAR Imagery

2016 ◽  
Vol 82 (7) ◽  
pp. 509-520 ◽  
Author(s):  
Majid Mahrooghy ◽  
James Aanstoos ◽  
RodrigoA. Nobrega ◽  
Khaled Hasan ◽  
NicolasH. Younan
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Soil Electrical Conductivity ◽  
Network Approach ◽  
Neural Network Approach ◽  
X Band ◽  
Sar Imagery
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