Research on the Prediction of Drug Sales Based on Levenberg-Marquardt Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1452-1456 ◽  
Author(s):  
Xue Feng Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Production Planning ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Experimental Results ◽  
Drug Production ◽  
Drug Sales ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

Prediction of drug sales trend is very important for the drug production planning and inventory. The paper studies the BP neural network and presents a kind of method based on reformative neural network to solve the issue of prediction of drug sales. Compare with traditional BP algorithm, the result reveals that this algorithm has structure rationalization and rapid constringency velocity. The experimental results demonstrate that the prediction model based on Levenberg_Marquardt algorithm is good at predicting drug sales.

Research on Temperature Field of Milling Insert with 3D Complex Groove

2007 ◽  
Vol 10-12 ◽  
pp. 374-378
Author(s):  
Ming Yang Wu ◽  
Q.X. Meng ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Temperature Field ◽  
Steady State ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Local Minimum ◽  
State Characteristic ◽  
Key Technology ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

Prediction of temperature field is a key technology to achieve the groove design and reconstruction of milling insert, predictive model of neural network is a new way to achieve the prediction of temperature field. According to the non-steady state characteristic of temperature field of milling insert, the paper puts forward a predictive model of temperature field of milling insert with 3D complex groove based on Levenberg-Marquardt algorithm of BP neural network, and it overcomes the disadvantage that traditional neural network is easy to fall into local minimum. The predictive results show that this predictive model can converge quickly and predict accurately.

Prediction of Oilfield Cementing Quality Based on Levenberg-Marquardt BP Algorithm

2011 ◽  
Vol 217-218 ◽  
pp. 1032-1035
Author(s):  
Yu Xue Wang ◽  
Shao Hua Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Improved Algorithm

In this paper an improved algorithm of BP neural network --- Levenberg-Marquardt (LM) algorithm is introduced, and the simulation predictions of oilfield cementing quality is done by using this method. Finally, a practical example verified the feasibility of the presented method.

Research on Pattern Recognition Based on BP Neural Network

2011 ◽  
Vol 282-283 ◽  
pp. 161-164
Author(s):  
Yong Lin Wang ◽  
Yan Liu ◽  
Sheng Bing Che
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Pattern Classification ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Fault Tolerant ◽  
Bp Algorithm ◽  
Experimental Results ◽  
Learning Capacity ◽  
Neural Network Algorithm

BP neural network has strong fault-tolerant and adaptive learning capacity, so it is widely used in pattern recognition. Based on the classic BP neural network, parameters of the BP algorithm has been optimized, which achieved a classification based on the improved BP neural network algorithm. By discussing the use of BP neural network in the application of pattern classification recognition, this paper detailedly studies the recognition effect of various parameters. Experimental results show that the improved algorithms has very good practical value.

Application of BP Neural Network in the Reliability Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3814-3818 ◽  
Author(s):  
Wei Jiang ◽  
Meng Zhang ◽  
Zhi Ling Chen ◽  
Yun Liu ◽  
Ning Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Reliability Prediction ◽  
Reliability Parameters ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Toolbox

Using neural network BP algorithm and the neural network toolbox of MATLAB, this paper presented a new reliability prediction model of the products. Its processes included that confirming training samples, putting up the network that was initialized, training the network and predicting reliability parameters of the products. At last reliability parameters of an example were predicted with the reliability prediction, the prediction effect was more perfect.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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