Agent based adaptive firefly back-propagation neural network training method for dynamic systems

2012 ◽  
Author(s):  
Sudarshan Nandy ◽  
Partha Pratim Sarkar ◽  
Ajith Abraham ◽  
Manoj Karmakar ◽  
Achintya Das ◽  
...  
Keyword(s):  
Neural Network ◽  
Dynamic Systems ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Method ◽  
Neural Network Training ◽  
Agent Based ◽  
Network Training

Forecasting Stock Price Based on Back Propagation Neural Network

2014 ◽  
Vol 1006-1007 ◽  
pp. 1031-1034
Author(s):  
Li Zhang ◽  
Qing Yang Xu ◽  
Chao Chen ◽  
Zeng Jun Bao
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Method ◽  
Price Forecast ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Hidden Layer ◽  
And Training

The stock market is a nonlinear dynamics system with enormous information, which is difficult to predict effectively by traditional methods. The model of stock price forecast based on BP Neutral-Network is put forward in this article. The paper try to find the way how to predictive the stock price. Exhaustive method is used for the hidden layer neurons and training method determination. Finally the experiment results show that the algorithm get better performance in stock price prediction.

The Defect Classification of TFT-LCD Array Photolithography Process via Using Back-Propagation Neural Network

2013 ◽  
Vol 378 ◽  
pp. 340-345
Author(s):  
Shih Feng Chen ◽  
Chin Chih Lai
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Defect Classification ◽  
Large Area ◽  
Fifth Generation ◽  
Network Training ◽  
Optical Inspection ◽  
Photolithography Process

This research is conducted mainly by using the Auto Optical Inspection (AOI) in the fifth generation TFT-LCD factory. In the development of detect-classification system, we designed the back-propagation neural network which combined with Visual Basic as the interface and MATLAB as an image-processing tool. The system is able to determine and display the detected results. The defect classification mainly designed to detect and classify the following defects: the second layer of the photo resist residue (AS-Residue), the second layer of large-area photo resist residue (AS-BPADJ), and the third layer of photo resist residue (M2-residue) in the Array Photolithography Process. Finally, the result is shown the fact that without the complicated processing procedures, the four defects in the TFT-LCD Array Photo Process can be precisely and quickly classified by imaging processing and back-propagation neural network training. As result, it is feasible to reduce the costs and the risk of human judgments.

Risk Warning of High-Tech Enterprise Independent Innovation Based on Chaotic Neural Network

2013 ◽  
Vol 568 ◽  
pp. 179-185
Author(s):  
Zhi Yong Wu ◽  
Hong Mei Chen ◽  
Xiu Hui Qi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Evaluation Index System ◽  
High Tech ◽  
Independent Innovation ◽  
Innovation Activities ◽  
Network Training ◽  
Risk Warning ◽  
Warning Model

Risk warning evaluation index system of independent innovation is established according to the process of innovation activities of high-tech enterprise, Chaotic Analysis Method is introduced into the BP(Back Propagation)Neural Network Model to research the early risk warning of high-tech enterprises independent innovation, the empirical results show that the integration of early warning model is feasible and effective, and significantly improve the convergence speed of network training, to some extent, avoid getting into local minimum.

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