A new synthesis approach for feedback neural networks based on the perceptron training algorithm

1997 ◽  
Vol 8 (6) ◽  
pp. 1468-1482 ◽  
Author(s):  
D. Liu ◽  
Z. Lu
Keyword(s):  
Neural Networks ◽  
Training Algorithm ◽  
New Synthesis ◽  
Feedback Neural Networks ◽  
Synthesis Approach

STORING LIMIT CYCLES USING DELAYED FEEDBACK NEURAL NETWORKS

2008 ◽  
Vol 11 (03) ◽  
pp. 433-442 ◽  
Author(s):  
GUIKUN WU ◽  
HONG ZHAO
Keyword(s):  
Neural Networks ◽  
Limit Cycles ◽  
Storage Capacity ◽  
Delayed Feedback ◽  
Training Algorithm ◽  
Correlation Learning ◽  
Global Training ◽  
Feedback Neural Networks

We show that the delayed feedback neural networks for storing limit cycles can be trained using a global training algorithm. It is found that the storage capacity of the networks is in proportion to delay length as in the networks trained by the correlation learning based on Hebb's rule, but is much higher than in the latter. The generalization capacity of the networks is also higher than in the latter. Another interesting finding is that the spurious states or unwanted attractors totally disappear in the networks trained by the global training algorithm if the memory limit cycles are sufficiently long. The dynamics of the networks is investigated as a function of the length of limit cycles.

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.

scholarly journals A Supervised Stdp-Based Training Algorithm for Living Neural Networks

2018 ◽  
Author(s):  
Yuan Zeng ◽  
Kevin Devincentis ◽  
Yao Xiao ◽  
Zubayer Ibne Ferdous ◽  
Xiaochen Guo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Training Algorithm

scholarly journals Machine-learning in astronomy

2014 ◽  
Vol 10 (S306) ◽  
pp. 279-287 ◽  
Author(s):  
Michael Hobson ◽  
Philip Graff ◽  
Farhan Feroz ◽  
Anthony Lasenby
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Gamma Ray ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Data Description ◽  
Astronomical Data ◽  
Machine Learning Methods ◽  
Network Training

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

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