scholarly journals A new model for indoor propagation prediction using genetic algorithm

2008 ◽  
Vol 5 (24) ◽  
pp. 1067-1073
Author(s):  
Elif Aydin
Keyword(s):  
Genetic Algorithm ◽  
Indoor Propagation ◽  
New Model ◽  
Propagation Prediction

scholarly journals Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

2016 ◽  
Vol 8 (8) ◽  
pp. 168781401666347 ◽  
Author(s):  
Milan Eric ◽  
Miladin Stefanovic ◽  
Aleksandar Djordjevic ◽  
Nikola Stefanovic ◽  
Milan Misic ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Production Process ◽  
Parameter Optimization ◽  
Process Parameter ◽  
Classification Method ◽  
Process Parameter Optimization ◽  
New Model ◽  
Model Based ◽  
Abc Classification

Research on Surface Roughness Prediction Model Based on Genetic Algorithm for Optical Ultra-Precision Machining

2007 ◽  
Vol 10-12 ◽  
pp. 369-373
Author(s):  
Jian Jun Du ◽  
Chi Fai Cheung ◽  
Suet To ◽  
Z.Y. Liu
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Linear Regression ◽  
Precision Machining ◽  
Mathematics Model ◽  
Simulation Test ◽  
New Model ◽  
Roughness Prediction ◽  
Ultra Precision

In this paper a dynamic non-linear mathematics model is proposed to predict the surface roughness in optical ultra-precision machining, which can be automatically built by evoling computer program of genetic algorithm. The new model can improve the fitting and predicting accuracy, compared with the traditional linear regression model. The numerical simulation test proves the effectiveness and accuracy of new model.

scholarly journals Measurement and evaluation of radar cross section for furniture in an indoor propagation channel

2014 ◽  
Vol 12 ◽  
pp. 273-278 ◽  
Author(s):  
M. M. Maw ◽  
P. Supanakoon ◽  
S. Promwong ◽  
J. Takada
Keyword(s):  
Transfer Function ◽  
Cross Section ◽  
Indoor Environment ◽  
Radar Cross Section ◽  
Ultra Wideband ◽  
Indoor Propagation ◽  
Measurement And Evaluation ◽  
Propagation Prediction ◽  
Ridged Waveguide ◽  
Radar Equation

Abstract. This paper has attempted to evaluate the radar cross section (RCS) of two furniture items in an indoor environment in a frequency range of 3–7 GHz of the ultra-wideband (UWB) range. The RCS evaluation is achieved through an extended version of the radar equation that incorporates the channel transfer function of scattering. The time-gating method was applied to remove the multipath effect, a phenomenon which typically occurs in the indoor environment. Two double-ridged waveguide horn antennas for both vertical and horizontal polarizations were used to obtain the transfer function of scattering of the furniture prior to analysis in order to derive their bistatic RCS. The RCS results validate the applicability of the proposed extended radar equation to the indoor propagation prediction.

New Method for Construction Land Prediction Incorporating Genetic Algorithm and Support Vector Machines

2011 ◽  
Vol 383-390 ◽  
pp. 1629-1634
Author(s):  
Yi Yong Luo ◽  
Li Ting Zhang ◽  
Hao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Supply And Demand ◽  
Support Vector ◽  
Construction Land ◽  
New Model ◽  
Vector Machines ◽  
Land Supply And Demand

Considering the increasingly tense relationship between construction land supply and demand, we study the inherent rules and the spatial evolution in construction land use. In order to solve the problem of parameter optimization effectively, we analysis the fundamental theory of Support Vector Machine and finally accomplish the combination of genetic algorithm and support vector machine. Meanwhile we apply this model to analysis the construction land use and propose a new model, which is based on the support vector machines with genetic algorithm, for construction land evolution. Taking Guandu district in Kunming, Yunnan as a case, we find out that the new model is far superior to recent models in terms of predicting accuracy, algorithm complexity and computational efficiency. And therefore, we believe that this is highly precise, practical and efficient model for forecasting construction land use and evolution.

scholarly journals New Optimal Weight Combination Model for Forecasting Precipitation

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Song-shan Yang ◽  
Xiao-hua Yang ◽  
Rong Jiang ◽  
Yi-che Zhang
Keyword(s):  
Genetic Algorithm ◽  
Forecast Accuracy ◽  
Prediction Method ◽  
Optimal Weight ◽  
New Model ◽  
Combination Model ◽  
Precipitation Forecasting ◽  
Optimal Weights ◽  
Ar Models ◽  
Beijing China

In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.

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