Neural network approaches for perception and action

1997 ◽  
pp. 356-378 ◽  
Author(s):  
Helge Ritter
Keyword(s):  
Neural Network ◽  
Perception And Action ◽  
Network Approaches

Neural network approaches for multi-spectral missile discrimination

2008 ◽  
Author(s):  
M. Can Altinigneli ◽  
Sabino Gadaleta
Keyword(s):  
Neural Network ◽  
Network Approaches

scholarly journals Outlier detection in scatterometer data: neural network approaches

2003 ◽  
Vol 16 (3-4) ◽  
pp. 419-426 ◽  
Author(s):  
Robert J. Bullen ◽  
Dan Cornford ◽  
Ian T. Nabney
Keyword(s):  
Neural Network ◽  
Outlier Detection ◽  
Network Approaches

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

Neural Network Approaches to Data Fusion

1995 ◽  
pp. 819-826 ◽  
Author(s):  
J. Yim ◽  
S. S. Udpa ◽  
L. Udpa ◽  
M. Mina ◽  
W. Lord
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Network Approaches

scholarly journals Comparison of two neural network approaches to modeling processes in a chemical reactor

2019 ◽  
Vol 23 (Suppl. 2) ◽  
pp. 583-589 ◽  
Author(s):  
Tatiana Shemyakina ◽  
Dmitriy Tarkhov ◽  
Alexander Vasilyev ◽  
Yulia Velichko
Keyword(s):  
Neural Network ◽  
Shooting Method ◽  
Chemical Reactor ◽  
Euler Method ◽  
Error Functional ◽  
Non Linear ◽  
Test Points ◽  
Linear Differential ◽  
Hidden Layer ◽  
Network Approaches

In this paper, we conduct the comparative analysis of two neural network approaches to the problem of constructing approximate neural network solutions of non-linear differential equations. The first approach is connected with building a neural network with one hidden layer by minimization of an error functional with regeneration of test points. The second approach is based on a new continuous analog of the shooting method. In the first step of the second method, we apply our modification of the corrected Euler method, and in the second and subsequent steps, we apply our modification of the St?rmer method. We have tested our methods on a boundary value problem for an ODE which describes the processes in the chemical reactor. These methods allowed us to obtain simple formulas for the approximate solution of the problem, but the problem is special because it is highly non-linear and also has ambiguous solutions and vanishing solutions if we change the parameter value.

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