基于动态模糊神经网络的淬火炉温度控制系统

大型淬火炉温度控制系统是复杂的非线性系统,很难建立其精确的数学模型,该文用动态模糊神经网络建立了淬火炉的智能温度控制系统,用基因长度可变的遗传算法(GA),自动调节模糊神经网络的权值和隶属函数的参数及推理规则数目,实现对隶属函数和推理规则的优化。实际应用结果表明,该方法能够提高淬火炉温度的控制精度,温度上升段,超调量小于4℃,保温阶段,温度变化范围在设定值的±2℃内,满足淬火炉对升温速度和恒温过程的精度要求。     

赤霉素对紫薇种子萌发的影响

研究了紫薇(Lagerstroemia indica L.)在不同浓度的赤霉素(GA3)、不同浸种时间处理下对种子萌发的影响。结果表明:GA3可以促进紫薇种子萌发,使用GA3处理的种子发芽率均高于CK;相同条件下200 mg/L浓度的GA3处理紫薇种子发芽率最高,发芽率为60%,明显高于100 mg/L和500 mg/L浓度的GA3处理的种子;GA3处理时间对紫薇种子的发芽率影响不明显。     

遗传算法综述

回顾了遗传算法的发展历程，并对遗传算法的基本原理及特点作了简要阐述；进一步指出了遗传算法存在的问题及相应的改进措施；讨论了遗传算法在实际中的应用。     

基于遗传算法的工艺决策模式的探索

本文通过对遗传算法和工艺决策理论的分析,提出了基于遗传算法的工艺决策方式。该方式具有优化、收敛性快等优点。     

LS-SVM model based nonlinear predictive control for MCFC system

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.     

Improved genetic operator for genetic algorithm

The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some extent, can result in premature convergence of genes (PCG) in the same locus. The above drawback of the traditional mutation operator causes the loss of critical alleles. Inspired by digital technique, we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can effectively prevent premature convergence, and can provide a wide selection range of control parameters for GA.     

基于GA与SA的社区检测优化算法研究

社区结构是网络最重要的属性之一,近年来社区检测受到极大关注,出现了很多社区发现算法。模块度是衡量社区划分好坏的重要指标,但是其分辨率却有一定局限性。将模块度中加入一个可调参数,根据社区结构调整参数更适合于需求不同的社区检测。随着网络规模的扩大,社区发现算法既要有较高的准确性,又要有很低的时间复杂性。提出一种发现算法GASA,该算法将遗传变异与模拟退火相结合,既有遗传算法的全局搜索能力,又有模拟退火算法的局部搜索能力。该算法用于社区检测优势明显,检测到的社区更接近真实社区。     

应用于液压集成块优化的一种混合遗传-退火算法

This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks (HMB). Due to the limitation of its local search ability of genetic algorithm (GA) in solving a massive combinatorial optimization problem, simulated annealing (SA) is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.     

Parameter optimization model in electrical discharge machining process

Electrical discharge machining （EDM） process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper, artificial neural network （ANN） and genetic algorithm （GA） are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate （MRR） and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.     

考虑有回收中转站的医疗废物三层回收网络优化研究

为全面覆盖医疗废物产生源,降低医疗废物暴露的危害,提出医疗废物三层回收网络结构,并构建三层回收网络优化模型。模型以最小化总回收成本为目标,总回收成本包括中转站固定成本、医废接收成本以及医废转运成本。运用LINGO软件包对模型第一层进行求解,第二层和第三层用遗传算法进行求解。最后,通过算例验证模型及算法的有效性。