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J. Comput. Sci. Eng. Vol.
11 (2014)
413-437 |
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Dynamic System Method for Solving Inverse Problems in Heat Conduction Equations
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Junli Zhang and Tingting Sheng
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J. Comput. Sci. Eng.
11 (2014) 413-417
—Published
March 21, 2014 |
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Abstract:Dynamic
system method is an effective method for solving
algebraic system. It is not only be applied to
well-posed operator equation but also is suitable for
nonlinear and ill-posed operator equations. In this
paper, dynamic system method is used to solve the
inverse problem of heat conduction equations, and
competes with classic Tikhonov regularization
algorithms. The numerical tests demonstrate dynamic
system method is a fast and simply algorithms. |
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Keywords:Dynamical
Systems Methods; Inverse problems; Heat Conduction
Equations; Stability.
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Quantitative structure-activity relationship study on
toxicity to tetrahymena pyriformis of aniline derivatives
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Tianyang Gao |
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J. Comput. Sci. Eng. 11 (2014) 3418-422 — Published April
20, 2014
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Abstract:
A quantitative structure-activity relationship was
developed by heuristic method (HM) and radial basis
function neural network (RBFNN) to study the tetrahymena
pyriformis toxicity (–logIGC50) of 48 aniline compounds.
The statistical parameters provided by the HM model were
R2 = 0.934; F = 159.96; RMS = 0.1761 for the training
set and R2 = 0.894; F = 67.309; RMS = 0.2613 for the
test set. The non-linear RBFNN model gave a better
result R2 = 0.968; F = 1087.692; RMS = 0.1159 for the
training set and R2 = 0.910; F = 80.905; RMS = 0.2106
for the test set. Cross-validation was used to evaluate
both models. |
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Keywords:Anilines;
Quantitative structure-property relationship (QSPR);
Heuristic method (HM); Radial basis function neural network
(RBFNN). :
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Load forecasting utilizing an
improved Wavelet Neural Network (WNN) model |
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Qingxiang Ou
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J. Comput. Sci. Eng. 11 (2014) 423-427 — Published April
21, 2014
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Abstract:Short-term
load forecasting is for the power system and has
great significance for the operation stable stir and
development of the power system. Wavelet neural
network model is an effective method to solve the
problem of power system load forecasting. In this
paper, increasing momentum term was employed to
improve the traditional wavelet neural network
model. Increasing momentum term could enhance
learning efficiency of network and propose an
improved wavelet neural network model which was
applied to short-term load forecasting in a certain
area of Beijing. The results show that the improved
Wavelet neural network model has a better prediction
performances and more suitable for short-term load
forecasting, compared with the traditiona model.
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Keywords:Load
forecasting; Wavelet neural network; Momentum term;
Prediction performance.
:
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CSE-PDF |
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Page start 328 A Survey on Selfishness
Incentive Mechanism in Opportunistic Networks |
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Li Liu
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J. Comput. Sci. Eng. 11 (2014) 428-431 — Published April
21, 2014
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Abstract:Opportunistic
Networks enable mobile devices to communicate in an
environment where contemporaneous end-to-end paths are
unavailable or unstable. Message delivery in
Opportunistic Networks is implemented through store
carry and forward fashion by use of short message
transmission technologies such as Bluetooth or WiFi. To
stimulate cooperation and prevent selfish behavior is an
important challenge in Opportunistic Networks. This
paper surveys the recent incentive mechanisms in
Opportunistic Networks. |
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Keywords:
DTNs; Incentive Mechanism; Selfishness.
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Fuzzy GM (1, 1) Model and Application
in Building Settlement Analysis |
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Huizhou Li
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J. Comput. Sci. Eng. 11 (2014) 432-437 — Published April 21, 2014
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Abstract:In
order to improve the prediction accuracy, GM (1, 1)
model is improved, and a fuzzy GM (1, 1) prediction
model is presented in this paper, named as FGM (1, 1).
In this method, a fuzzy function is introduced into GM
(1, 1) mode, by which the data of time series is fuzzed
to optimize the selection of data, and the forecasting
of history data ′fart her is more weight ′can be
achieved. Building settlement analysis results with the
application of FGM (full name) (1, 1) show that this
forecast method is effective and reliable, which offers
new approach to improving forecast accuracy. |
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Keywords:Fuzzy
GM (1, 1); Fuzzy function; Settlement analysis.
:
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—— from ACSS |
