Optimization of Optical Multilayer Based on Genetic Algorithm and Nonlinear Least Square Method

2002 ◽  
Author(s):  
Zhigang Fan ◽  
Hongbing Li ◽  
Aihong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Least Square Method ◽  
Least Square

Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Non Linear ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

GMDH Network Model Based on Simulated Annealing and Genetic Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1370-1373
Author(s):  
Xiao Ling Zhang ◽  
Li Kun Zou
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Network Model ◽  
Simulated Annealing Algorithm ◽  
Least Square Method ◽  
Least Square ◽  
Model Based ◽  
Simulated Annealing Genetic Algorithm ◽  
Global And Local ◽  
Combined Algorithm

According to the traditional UMDH network modeling with the least square method to recognize parameters ,it's easy to fall into local minimum ,and with the result that the prediction effect is not ideal. This paper puts forward to combine the simulated annealing algorithm and genetic algorithm, and introduces the combined algorithm to the UMDH network which is used to identify some of its description type coefficient. In this paper ,it describes the simulated annealing genetic algorithm ,and constructs the UMDH network model based on this algorithm, and the model is applied to the simulation of debris flow prediction research ,forecast average relative error reached 3. 54%. The results show that the algorithm not only ensuring the global optimization but also preventing premature convergence, improve the UMDH network model of global and local searching optimal ability further.

Path Loss Model Optimization Using Stochastic Hybrid Genetic Algorithm

2018 ◽  
Vol 7 (4.10) ◽  
pp. 464
Author(s):  
A. Bhuvaneshwari ◽  
R. Hemalatha ◽  
T. SatyaSavithri
Keyword(s):  
Genetic Algorithm ◽  
Path Loss ◽  
Least Square Method ◽  
Mobile Radio ◽  
Propagation Model ◽  
Least Square ◽  
Hybrid Optimization ◽  
Loss Model ◽  
Path Loss Model ◽  
Weighted Least Square

In the context of modeling the propagation of mobile radio signals, optimizing the existing path loss model is largely required to precisely represent the actual propagation medium. In this paper, a hybrid tuning approach is proposed by merging the stochastic Weighted Least Square method and Genetic algorithm. The proposed hybrid optimization is employed to optimize the parameters of Cost 231 Hata propagation model and is validated by cellular field strength measurements at 900 MHz in the sub urban region. The hybrid optimization is compared with optimized results of Weighted Least Square method and Genetic algorithm. The least values of Mean Square error (0.2702), RMSE (0.4798) and percentage Relative error (3.96) justify the tuning precision of the hybrid method. The proposed optimization approach could be used by network service providers to improve the quality of service and in mobile radio network planning of 900 MHz band for 4G LTE services.  

The Boiler Temperature Field Reconstruction Algorithm Based on Genetic Algorithm

2011 ◽  
Vol 88-89 ◽  
pp. 269-273
Author(s):  
Cheng Zhi Li ◽  
Fu Qun Shao ◽  
Zhe Kan ◽  
Hai Xiang Fan
Keyword(s):  
Genetic Algorithm ◽  
Temperature Field ◽  
Least Square Method ◽  
Reconstruction Algorithm ◽  
Acoustic Method ◽  
Least Square ◽  
Experimental Result ◽  
Field Reconstruction ◽  
Single Path ◽  
Temperature Field Reconstruction

The traditional power station boiler temperature field reconstruction algorithm is sensitive to the time of flight. In the boiler movement, the temperature field has symmetric distribution feature within the boiler. On the basis of the boiler temperature field reconstruction fundamental by using the acoustic method, the paper presents a new two dimension temperature field reconstruction algorithm, which combines the single path method and genetic algorithm. Firstly, the algorithm makes sure the temperature distribution by using single path function. It uses the points denote the temperatures on each path, and plots the mesh, which can represent the temperature preliminary distribution, by using the Bezier spline principle and linear multistep integration. Finally, the surface mesh is Interpolated and fitted by using genetic algorithm. The experimental result proved that, compared to the least square method, the new reconstruction algorithm has the feature of higher accuracy and higher reconstruction speed.

scholarly journals Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Xing Zong-yi ◽  
Qin Yong ◽  
Pang Xue-miao ◽  
Jia Li-min ◽  
Zhang Yuan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Distance Measure ◽  
Rbf Neural Network ◽  
Least Square Method ◽  
Least Square ◽  
Nonlinear Characteristics ◽  
Electrohydraulic System ◽  
Comparison Results ◽  
Depth Control

The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.

Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Vol 134 (3) ◽  
Author(s):  
Y.G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Design Point ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A nonlinear multiple point genetic algorithm based performance adaptation developed earlier by the authors using a set of nonlinear scaling factor functions has been proven capable of making accurate performance predictions over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain the optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of the trial and error process. In this paper, an improvement on the present adaptation method is presented using a least square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the least square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

Research on Boundary Extraction of STL Models based on Genetic Algorithm

2012 ◽  
Vol 566 ◽  
pp. 78-81
Author(s):  
Jing Bin Hao ◽  
Zhong Bin Wang ◽  
Hai Feng Yang ◽  
Zhong Kai Li
Keyword(s):  
Genetic Algorithm ◽  
Extraction Method ◽  
Least Square Method ◽  
Least Square ◽  
Threshold Method ◽  
Boundary Extraction ◽  
Large Complex ◽  
Complex Models ◽  
Curvature Information ◽  
Best Fit

To efficiently decompose a large complex STL model, an improved boundary extraction method is proposed based on genetic algorithm. Three curvature parameters (dihedral angle, perimeter ration and convexity) were used to estimate the surface curvature information. Genetic Algorithm (GA) is used to determinate the threshold of feature edge. The discrete feature edges are grouped and filtered using the best-fit plane (BFP), which is calculated by Least Square Method (LSM). Several experimental results demonstrate that the amount of feature edges is about half of the preset threshold method, and useful feature edges were reserved. The extracted feature boundaries can be directly used to decompose large complex models.

A New Method for Evaluating the Profile of Large Paraboloidal Antennas

2014 ◽  
Vol 541-542 ◽  
pp. 1408-1413 ◽  
Author(s):  
Yi Ran Jiang
Keyword(s):  
Genetic Algorithm ◽  
Least Square Method ◽  
Position Error ◽  
Least Square ◽  
New Method ◽  
Adaptive Adjustment ◽  
Theoretical Contour

In order to solve the problem of installation and measuring errors in the profile evaluation of large parabolic antennas, this paper proposes a new method for evaluating the profile of large paraboloidal antennas based on genetic algorithm and least square method. This method can effectively realize the adaptive adjustment of the measured points and the theoretical contour in the process of evaluating profile, which can eliminate the position error between the measured points and the theoretical contour that influences on the results of the profile evaluation. The availability of this method has been proved by simulation.

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