Pyrolysis of cattle dung: model fitting and artificial neural network validation approach

Author(s):  
Muhammad Ashraf ◽  
Zaheer Aslam ◽  
Naveed Ramzan ◽  
Umair Aslam ◽  
Abdullah Khan Durrani ◽  
...  
2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.


2011 ◽  
Vol 347-353 ◽  
pp. 16-21
Author(s):  
Yang Xiao ◽  
Qiu Liang Zhang ◽  
Zhi Hua Zhao ◽  
Cheng Shuang Song

Make natural forest birch at Da Qingshan nature reserves in Inner Mongolia as the research object. The data is from the National Second-Class investigation data in 2006 by Inner Mongolia survey and design institute of forestry in 2006. Take 8 forest centre as study areas. All these datas would be sifted, and chosen the datas which the varieties of trees is white birch and the formation of the tree species is pure forest classes. The total of data is 4785. Use of Matlab software log-the sigmoid type function (logsig) and linear function (purelin) for the role of neurons. Based on the function of the concept of stand growth model, we choose age requirement (A), status level (N) and crown density (S) as input variables and the forest accumulation per hectare (M) as output variables to build and ttrain the stand growth BP artificial neural network model. And test the model fitting precision and inspection accuracy , the model fitting precision is 99.93%, inspection accuracy is 97.79%, these show that neural network modeling has better fitting precision and adaptability for the stand growth, and has good prediction ability.


2012 ◽  
Vol 446-449 ◽  
pp. 3247-3251
Author(s):  
Ning Gao ◽  
Xi Min Cui ◽  
Cai Yun Gao

This paper describes the procedure of a hybrid approach based on grey model and artificial neural network (GM&ANN) to analysis and forecast of deformation data. The GM&ANN is formulated into three steps:(1)according to the monotonously increasing characteristics and the nonlinear characteristics of deformation time series, total deformation can be divided into tendency part and stochastic part.(2) use GM(1,1)to fit the trend of the data and obtain the residual series, on this basis by using artificial neural network to fit the stochastic part (residual series) .Then the forecasting value of deformation is obtained by adding the calculated predictive displacement value of each sub-stack. (3) validate the model. The results of experiments show that this hybrid has higher performances not only on model fitting but also on forecasting and therefore can be applied to deformation data processing.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

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