NEW FAST TRAINING ALGORITHM SUITABLE FOR HARDWARE KOHONEN NEURAL NETWORKS DESIGNED FOR ANALYSIS OF BIOMEDICAL SIGNALS

2009 ◽  
Keyword(s):  
Neural Networks ◽  
Training Algorithm ◽  
Biomedical Signals ◽  
Fast Training ◽  
Kohonen Neural Networks

An Improved Training Algorithm for Quantum Neural Networks

2014 ◽  
Vol 35 (7) ◽  
pp. 1630-1635
Author(s):  
Yi-peng Zhang ◽  
Liang Chen ◽  
Huan Hao
Keyword(s):  
Neural Networks ◽  
Training Algorithm

TRAINING ALGORITHM OF UNIFORM PARZEN WINDOW NEURAL NETWORKS

2008 ◽  
Vol 1 (3) ◽  
pp. 178-187
Author(s):  
Chao-Yin Hsiao ◽  
Shan-Hung Hsieh
Keyword(s):  
Neural Networks ◽  
Training Algorithm ◽  
Parzen Window

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

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