Title :
Reconfiguration of Spiking Neural Network for Optimization with Applications to Image Processing
Author :
Chaturvedi, Sushil ; Khurshid, A.A. ; Dorle, S.S.
Author_Institution :
E&C Eng., PIET, Nagpur, India
Abstract :
This paper depicts the restructuring of different models of third generation of Artificial neural network, that is, the spiking neural networks for image processing applications. The proposed work aims towards implementation of a novel algorithm using different models of Spiking Neural Networks which will improve upon the optimization results in the field of image processing. In this paper, we focus on various evaluation parameters like mean square error, mean absolute error peak signal to noise ratios as well as enhance the output using ANN as wellas Leaky Integrate and firing Model of Spiking Neural Networks.
Keywords :
image processing; mean square error methods; neural nets; optimisation; ANN; artificial neural network; firing model; image processing; leaky integrate; mean absolute error peak signal to noise ratios; mean square error; optimization; spiking neural network reconfiguration; Artificial neural networks; Biological neural networks; Computational modeling; Image processing; Market research; Pattern recognition; LIF model; MAE; MSE; PSNR; SNN;
Conference_Titel :
Emerging Trends in Engineering and Technology (ICETET), 2013 6th International Conference on
Conference_Location :
Nagpur
Print_ISBN :
978-1-4799-2560-5
DOI :
10.1109/ICETET.2013.54