Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

2011 ◽  
Vol 78 (2) ◽  
pp. 464-475 ◽  
Author(s):  
Jalil Asadisaghandi ◽  
Pejman Tahmasebi
Keyword(s):  
Neural Network ◽  
Comparative Evaluation ◽  
Learning Algorithms ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pvt Properties ◽  
Empirical Correlations ◽  
Neural Network Learning ◽  
Network Learning

Based on Privacy Preserving for Back-Propagation Neural Network Learning Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 857-862
Author(s):  
Jian Wang
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Learning Algorithms ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Privacy Preserving ◽  
Complex Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Partitioned Data

Neural network learning algorithms are widely used in medical diagnosis, bioinformatics, intrusion detection, homeland security and other fields. The common of these applications is that all of them need to extract patterns and predict trends from a large number of complex data. In these applications, how to protect the privacy of sensitive data and personal information from disclosure is an important issue. At present, the vast majority of existing neural network learning algorithms did not consider how to protect the data privacy in the process of learning. So this paper proposes two privacy-preserving back-propagation neural network protocols applied to horizontally partitioned data and vertically partitioned data separately. The two protocols are suitable for multiple participants in a distributed environment. The results of experiments show the difference of the test error rate between the proposed two protocols and the respective non-privacy preserving versions.

Local Learning Algorithms for Sequential Tasks in Neural Networks

1998 ◽  
Vol 2 (6) ◽  
pp. 221-227 ◽  
Author(s):  
Anthony Robins ◽  
◽  
Marcus Frean ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithms ◽  
Learning Task ◽  
Sequential Learning ◽  
Local Learning ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sequential Task ◽  
Global And Local

In this paper, we explore the concept of sequential learning and the efficacy of global and local neural network learning algorithms on a sequential learning task. Pseudorehearsal, a method developed by Robins19) to solve the catastrophic forgetting problem which arises from the excessive plasticity of neural networks, is significantly more effective than other local learning algorithms for the sequential task. We further consider the concept of local learning and suggest that pseudorehearsal is so effective because it works directly at the level of the learned function, and not indirectly on the representation of the function within the network. We also briefly explore the effect of local learning on generalization within the task.

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