The Application of Probabilistic Neural Network Model In the Green Supply Chain Performance Evaluation for Pig Industry

2010 ◽  
Author(s):  
He Kailun ◽  
Xu Huijun ◽  
Xu Maohua
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Performance Evaluation ◽  
Network Model ◽  
Neural Network Model ◽  
Probabilistic Neural Network ◽  
Green Supply Chain ◽  
Supply Chain Performance

scholarly journals An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Convergence Rate ◽  
Inventory Control ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Inventory Level ◽  
Bp Neural Network Model

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

scholarly journals Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zoran Stanković ◽  
Nebojša Dončov ◽  
Bratislav Milovanović ◽  
Ivan Milovanović
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Probabilistic Neural Network ◽  
Doa Estimation ◽  
Variable Number ◽  
Angular Distance ◽  
Far Field ◽  
The Neural Network ◽  
Two Stages

An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM) sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN) and the Multilayer Perceptron (MLP) networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA) estimation within the training boundaries which is illustrated on the appropriate example.

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