Genetic algorithms in mesh optimization for visualization and finite element models

This paper investigates the use of a genetic algorithm (GA) to perform the large-scale triangular mesh optimization process. This optimization process consists of a combination of mesh reduction and mesh smoothing that will not only improve the speed for the computation of a 3D graphical or finite element model, but also improve the quality of its mesh. The GA is developed and implemented to replace the original mesh with a re-triangulation process. The GA features optimized initial population, constrained crossover operator, constrained mutation operator and multi-objective fitness evaluation function. While retaining features is important to both visualization models and finite element models, this algorithm also optimizes the shape of the triangular elements, improves the smoothness of the mesh and performs mesh reduction based on the needs of the user.     

Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study

The use of multilayer ceramic capacitors (MLCCs) is increasing because they are surface-mountable and are used primarily in the expanding communication and computing market. In the MLCC manufacturing process, some 80% of the loss in yield is attributable to paste-printing quality problems. Improvement in the quality of MLCC screen-printing is therefore tactically and strategically important. This research extends existing MLCC screen-printing robust design results to search for a universal optimum solution. A metamodeling approach has been applied to solving a variety of optimization problems. This is an abstraction model form from a model. The abstracted model aims to reduce model complexity, and yet maintain validity. This work involved building a screen-printing quality metamodel, based upon fractional factorial experimental design data using a neural network approach—that were then solved by genetic algorithms. The empirical results are promising. The paper concludes with practical constraints and insights for management.     

Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms

This paper presents two different power system stabilizers (PSSs) which are designed making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a conventional PSS on different operating conditions and then, the relationship between these points and the PSS parameters is learned by the ANFIS. ANFIS will select the PSS parameters based on machine loading conditions. The first stabilizer is adjusted minimizing an objective function based on ITAE index, while second stabilizer is adjusted minimizing an objective function based on pole-placement technique. The proposed stabilizers have been tested by performing simulations of the overall nonlinear system. Preliminary experimental results are shown.     

Genetic algorithm design of neural network and fuzzy logic controllers

This paper discusses the design of neural network and fuzzy logic controllers using genetic algorithms, for real-time control of flows in sewerage networks. The soft controllers operate in a critical control range, with a simple set-point strategy governing “easy” cases. The genetic algorithm designs controllers and set-points by repeated application of a simulator. A comparison between neural network, fuzzy logic and benchmark controller performance is presented. Global and local control strategies are compared. Methods to reduce execution time of the genetic algorithm, including the use of a Tabu algorithm for training data selection, are also discussed. The results indicate that local control is superior to global control, and that the genetic algorithm design of soft controllers is feasible even for complex flow systems of a realistic scale. Neural network and fuzzy logic controllers have comparable performance, although neural networks can be successfully optimised more consistently.     

神经网络、遗传算法在发酵优化软件平台设计中的应用

本课题设计了基于DDE的VB、Excel和Matlab的发酵过程优化软件平台，并将该软件平台应用到多粘菌素发酵过程PH值的寻优。实践表明：该优化软件平台能够确定发酵过程被控参数的优化轨线，为优化控制提供一个主要目标。     

Sensitivity analysis and comparison of algorithms in preventive maintenance and replacement scheduling optimization models

In this research, new optimization models are developed to determine the optimal preventive maintenance and replacement schedules in repairable and maintainable systems. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability over the planning horizon. Experimental results of a sensitivity analysis on the optimization models are presented and evaluated. These experiments investigate the effect of the parameters on the structure of optimal preventive maintenance and replacement schedules in multi-component systems. Two factorial design experiments based on the cost associated with maintenance and replacement activities and reliability characteristic parameters are constructed and analyzed. In addition, a comprehensive experiment is designed to analyze and compare the efficiency and accuracy of the exact and metaheuristic algorithms.     

遗传算法和神经网络在白细胞自动识别中的应用

该文讨论并建立了一真彩色白细胞显微图像微机自动识别系统。针对传统组合优化方法用于特征选择的种种缺陷,提出了基于遗传算法的特征选择策略。根据细胞特征,笔者对传统的遗传算法做了相应的改进。最后运用选择特征子集来训练BP神经网络作为分类器。结果证明该方法对于分类系统的性能优化有明显效果。     

基于遗传算法的神经网络控制器的设计及应用

本文提出了一种改进的遗传算法进行神经网络的训练，它结合神经网络、遗传算法两种方法的优点，并用仿真实验表明该方法的有效性。用改进算法设计的神经网络控制器，来实现循环型制气过程优化控制，取得了令人满意的结果，验证了该方法具有良好的鲁棒性和控制精度。     

神经网络学习算法中的初始参数对泛化性能和效率的影响研究

该文的目的是研究影响神经网络的泛化性能和实时学习性能的因素。首先研究了初始权值的范围、学习率和正则项系数对泛化性能和学习速度的影响。基于此,提出了一种同时考虑这三种因素的混合学习方法,并动态地调整学习率和正则项系数。然后,通过几个不同问题的实例仿真,将几种不同的方法所取得的结果进行了比较,验证了所提出方法的有效性。最后是全文总结。     

神经网络在控制系统中的应用现状及展望

本文分析了神经网络在控制系统中的应用，并对目前几种较重要的神经网络控制现状进行了评述，最后对神经网络控制的发展作了展望。     

神经网络在控制系统中的应用现状及展望

本文分析了神经网络在控制系统中的应用,并对目前几种较重要的神经网络控制现状进行了评述,最后对神经网络控制的发展作了展望。     

三维物体表面三角划分的遗传算法

提出了一种基于遗传算法的从给定平行截面线重建物体表面的方法。该算法利用了三维表面三角形划分和遗传算法的基本原理,使用多目标适应函数,能够进行多目标优化从而满足不同的要求,算法还采有了新交叉与变异算子－三维多边形交叉与三维四边形变异。     

遗传算法在钟表机芯设计中的应用

在钟表机芯设计中，齿轮参数的优化设计是一个组合优化问题，很难用传统优化方法解决.遗传算法是一种基于生物进化原理的启发式搜索方法，近年来，它成功地解决了许多计算难题.使用该算法的难点是如何将具体问题映射成适于该算法的编码以及根据编码进行各种操作.该文对传动系统各齿轮参数序号进行编码，成功地解决了齿轮参数的优化设计问题，也为一般机械设计中传动系统参数的优化提供了经验.通过比较，利用遗传算法得出的参数比用专家系统得出的参数更优.     

The comparison of accommodative response and ocular movements in viewing 3D and 2D displays

Some people often appear asthenopia symptoms of eye fatigue, double vision, nausea, and dizziness while viewing 3D movies. By testing the changes of accommodation function and ocular movements during watching 3D and 2D movies, the factors in visual discomfort are confirmed in this study.20 subjects with normal visual acuity and binocular vision function view 3D and 2D movies with the same content for 30 min, and the amplitude of accommodation, binocular vergence ability, stereo-acuity, and tear break-up time of the subjects before and after viewing the films are measured. Furthermore, an open-field auto-refractor is utilized for synchronously testing the change of accommodative response while viewing 3D and 2D films, and Skalar IRIS tracking system is applied to record ocular movements through infrared positioning.In comparison with viewing 2D movies, the accommodative response and ocular movements reveal obvious changes while viewing 3D movies. The accommodation ability and binocular vergence ability obviously drop after viewing 3D movies; moreover, the stability of stereo-acuity and tear film also gets worse. The changes of such physiological factors might be the major cause of asthenopia.