A Comparative Study on Static Voltage Stability Analysis of Power System using Feed Forward Neural Network and Radial Basis Function Neural Network with Reduced Set of Inputs

2021 ◽  
Author(s):  
Nilesh P. Tanpaniya ◽  
Viren B. Pandya
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Power System ◽  
Comparative Study ◽  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Stability ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Static Voltage Stability

OFF-LINE SIGNATURE AUTHENTICATION USING RADIAL BASIS FUNCTION

2011 ◽  
Vol 25 (02) ◽  
pp. 207-225 ◽  
Author(s):  
K. R. RADHIKA ◽  
S. V. SHEELA ◽  
G. N. SEKHAR
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rejection Rate ◽  
Minimum Variance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
False Rejection Rate ◽  
Core Components ◽  
False Rejection

A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.

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