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DESCENT DIRECTION STOCHASTIC APPROXIMATION ALGORITHM WITH ADAPTIVE STEP SIZES
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作者 Zorana Luzanin Irena Stojkovska Milena Kresoja 《计算数学:英文版》 SCIE CSCD 2019年第1期76-94,共19页
A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed num... A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed number of previous noisy function values as a criterion for accepting good and rejecting bad steps.The scheine allows the algorithm to move in bigger steps and avoid steps proportional to 1/k when it is expected that larger steps will improve the performance.An algorithin with the new adaptive scheme is defined for a general descent direction.The ahnost sure convergence is established.The performance of new algorithm is tested on a set of standard test problems and compared with relevant algorithms.Numerical results support theoretical expectations and verify efficiency of the algorithm regardless of chosen search direction and noise level.Numerical results on probleins arising in machine learning are also presented.Linear regression problem is considered using real data set.The results suggest that the proposed algorithln shows proinise. 展开更多
关键词 UNCONSTRAINED OPTIMIZATION STOCHASTIC OPTIMIZATION STOCHASTIC APPROXIMATION NOISY function Adaptive step size DESCENT direction Linear regression model
Optimized cellular automaton for stand delineation 预览
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作者 Timo Pukkala 《林业研究:英文版》 CAS CSCD 2019年第1期107-119,共13页
Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source fores... Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m×16 m grid cells.Using these cells as simulation units in forest planning would lead to very large planning problems.This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations.This study developed a cellular automaton(CA)for aggregating grid cells into larger calculation units,which in this study were called stands.The criteria used in stand delineation were the shape and size of the stands,and homogeneity of stand attributes within the stand.The stand attributes were:main site type(upland or peatland forest),site fertility,mean tree diameter,mean tree height and stand basal area.In the CA,each cell was joined to one of its adjacent stands for several iterations,until the cells formed a compact layout of homogeneous stands.The CA had several parameters.Due to high number possible parameter combinations,particle swarm optimization was used to find the optimal set of parameter values.Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes.When the CA was optimized without any restrictions for its parameters,the resulting stand delineation consisted of small and irregular stands.A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands(<0.1 ha).However,there was within-stand variation in fertility class due to small-scale variation in the data.The stands delineated by the CA explained 66–87%of variation in stand basal area,mean tree height and mean diameter,and 41–92%of variation in the fertility class of the site.It was concluded that the CA develope 展开更多
关键词 Forest planning Particle SWARM OPTIMIZATION RASTER data SEGMENTATION Spatial OPTIMIZATION
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Adaptive optimization methodology based on Kriging modeling and a trust region method
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作者 Chunna LI Qifeng PAN 《中国航空学报:英文版》 SCIE EI CAS CSCD 2019年第2期281-295,共15页
Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for c... Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-regionbased refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO methods. 展开更多
关键词 AIRFOIL Design OPTIMIZATION KRIGING model Surrogate-based OPTIMIZATION TRUST-REGION method
Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods 预览
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作者 Jiajun Wang Tufan Kumbasar 《自动化学报:英文版》 CSCD 2019年第1期247-257,共11页
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou... Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs. 展开更多
关键词 BIG bang-big crunch (BBBC) INTERVAL type-2 fuzzy NEURAL networks (IT2FNNs) parameter OPTIMIZATION particle SWARM OPTIMIZATION (PSO)
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一种绿色能源小水线面双体无人艇优化初步研究 预览
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作者 汤旸 《江苏科技信息》 2019年第6期28-30,53共4页
文章针对小水线面双体无人艇,选取该无人艇的快速性、操纵性、耐波性和太阳能系统的目标函数,确定设计变量和约束条件的范围,建立了综合优化数学模型,并选取合适的优化算法,自主编写了一套优化设计软件,进行综合优化计算。先比较不同代... 文章针对小水线面双体无人艇,选取该无人艇的快速性、操纵性、耐波性和太阳能系统的目标函数,确定设计变量和约束条件的范围,建立了综合优化数学模型,并选取合适的优化算法,自主编写了一套优化设计软件,进行综合优化计算。先比较不同代数的粒子群算法的适应度函数值,而后选用粒子群算法作为主算法得出最好的5个个体信息,与自身及其他优化算法结合进行二次计算。最终得到小水线面双体船最优船型参数,研究结果可为小水线面双体无人艇各项性能优化的多目标、多变量及多约束条件综合优化问题提供参考。 展开更多
关键词 海洋 绿色能源 小水线面双体无人艇 优化 粒子群算法
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低展弦比CAES向心涡轮叶顶型线的正交设计优化 预览
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作者 王星 李文 +3 位作者 张雪辉 朱阳历 左志涛 陈海生 《风机技术》 2019年第1期11-21,I0008共12页
为降低叶顶泄漏损失,本文首次将NACA 翼型引入向心涡轮,并采用正交试验设计和计算流体动力学(CFD)方法获得了具有最优NACA 叶顶型线的向心涡轮叶片,并揭示了该叶片对叶顶泄漏损失的控制机理。结果表明,最优NACA 叶顶型线具有较大的前缘... 为降低叶顶泄漏损失,本文首次将NACA 翼型引入向心涡轮,并采用正交试验设计和计算流体动力学(CFD)方法获得了具有最优NACA 叶顶型线的向心涡轮叶片,并揭示了该叶片对叶顶泄漏损失的控制机理。结果表明,最优NACA 叶顶型线具有较大的前缘内接圆半径、较小的尾缘厚度,以及更靠近前缘的最大厚度位置。其内接圆半径和最大厚度位置对向心涡轮等熵效率的影响度也随叶顶间隙增加而增大。当叶顶间隙为8%出口叶高时,最优NACA叶顶型线可使向心涡轮等熵效率提高1.47%,并使向心涡轮能够在非设计工况下具有较高效率。该型线能够降低尾缘附近的叶顶泄漏速度,减弱泄漏流与主流掺混强度,使流动损失降低。 展开更多
关键词 Radial EXPANDER Optimization Blade Tip Profile CAES ORTHOGONAL Experiment Design
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An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization
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作者 LIU Ao DENG Xudong +2 位作者 REN Liang LIU Ying LIU Bo 《系统科学与复杂性学报:英文版》 SCIE EI CSCD 2019年第2期634-656,共23页
As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implement... As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence.In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate. 展开更多
关键词 EVOLUTIONARY algorithms FRUIT FLY OPTIMIZATION function OPTIMIZATION META-HEURISTICS
面向反应再生过程的量子粒子群多目标优化 预览
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作者 白竣仁 易军 +2 位作者 李倩 吴凌 陈雪梅 《化工学报》 EI CAS CSCD 北大核心 2019年第2期750-756,共7页
针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题,利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型;引入拥挤熵排序更新外部档案,精确估计非支配解... 针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题,利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型;引入拥挤熵排序更新外部档案,精确估计非支配解集分布性;构造自适应因子以动态调整吸引子,平衡算法的收敛性和多样性;再引入高斯变异进行分段式扰动,增强算法的局部搜索精度,最后求解该优化模型。对某厂催化裂化进行实验,得到轻质油吸收率76.22%,焦炭产率5.72%和硫化物排放量626mg/m3的结果,均优于其他比较算法,表明改进后的算法可以快速、准确地获得分布均匀的Pareto最优解,能有效解决反应再生过程多目标优化问题。 展开更多
关键词 催化 反应 控制 优化 量子粒子群优化算法 拥挤熵
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Modeling river water quality parameters using modified adaptive neuro fuzzy inference system 预览
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作者 Armin Azad Hojat Karami +2 位作者 Saeed Farzin Sayed-Farhad Mousavi Ozgur Kisi 《水科学与水工程:英文版》 EI CAS CSCD 2019年第1期45-54,共10页
Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improv... Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters. 展开更多
关键词 Water quality parameters ANFIS EVOLUTIONARY algorithm Particle SWARM OPTIMIZATION Ant COLONY OPTIMIZATION for continuous DOMAINS
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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization
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作者 Jia Wu Xiu-Yun Chen +3 位作者 Hao Zhang Li-Dong Xiong Hang Lei Si-Hao Deng 《电子科技学刊:英文版》 CAS CSCD 2019年第1期26-40,共15页
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.Several techni... Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.Several techniques have been developed and successfully applied for certain application domains.However,this work demands professional knowledge and expert experience.And sometimes it has to resort to the brute-force search.Therefore,if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method,it will greatly improve the efficiency of machine learning.In this paper,we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes.In this way,the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem.Bayesian optimization is based on the Bayesian theorem.It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function.A utility function selects the next sample point to maximize the optimization function.Several experiments were conducted on standard test datasets.Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models,such as the random forest algorithm and the neural networks,even multi-grained cascade forest under the consideration of time cost. 展开更多
关键词 BAYESIAN OPTIMIZATION GAUSSIAN process hyperparameter OPTIMIZATION MACHINE LEARNING
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Distributed Majorization-Minimization for Laplacian Regularized Problems 预览
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作者 Jonathan Tuck David Hallac Stephen Boyd 《自动化学报:英文版》 CSCD 2019年第1期45-52,共8页
We consider the problem of minimizing a block separable convex function(possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitti... We consider the problem of minimizing a block separable convex function(possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general,and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy. 展开更多
关键词 Convex OPTIMIZATION DISTRIBUTED OPTIMIZATION graphical networks LAPLACIAN REGULARIZATION
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循环水水质的控制与优化 预览
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作者 王新 许玉玲 祁伟 《聚氯乙烯》 CAS 2019年第4期16-18,共3页
介绍了氯碱装置循环水系统运行状况及指标控制方案,分析了循环水指标对装置运行情况的影响。
关键词 循环水 水质 指标 优化
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综采放顶煤工作面综合防尘技术应用研究
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作者 常华 《江西煤炭科技》 2019年第1期25-27,共3页
综采放顶煤作为厚煤层开采的一种重要采煤方法在当今厚煤层矿井中占有较大比例,也正是因为其采高较大,已完全超出传统喷淋设备的有效作用范围,使得综采放顶煤工作面粉尘问题严重,不仅影响安全生产,也对工人健康造成危害。通过研究分析... 综采放顶煤作为厚煤层开采的一种重要采煤方法在当今厚煤层矿井中占有较大比例,也正是因为其采高较大,已完全超出传统喷淋设备的有效作用范围,使得综采放顶煤工作面粉尘问题严重,不仅影响安全生产,也对工人健康造成危害。通过研究分析现有防尘技术及设备,探讨综采放顶煤工作面综合防尘技术,通过实践,有效减少了工作面粉尘浓度,对综采放顶煤、大采高采煤工作面的防尘技术研究具有一定借鉴意义。 展开更多
关键词 综采放顶煤 防尘 优化
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基于DSP的驾驶员疲劳实时预警系统设计
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作者 李照 舒志兵 《控制工程》 CSCD 北大核心 2019年第1期92-98,共7页
为设计并且实现了一个基于DSP的嵌入式车载疲劳检测系统。构建了由CCD,TMS320DM6437,红外光源组成的硬件平台。结合PERCLOS模型来进行驾驶员疲劳程度的判断。针对AdaBoost算法中不适合TMS320C6437处理器特点的大量浮点运算,在保持AdaBo... 为设计并且实现了一个基于DSP的嵌入式车载疲劳检测系统。构建了由CCD,TMS320DM6437,红外光源组成的硬件平台。结合PERCLOS模型来进行驾驶员疲劳程度的判断。针对AdaBoost算法中不适合TMS320C6437处理器特点的大量浮点运算,在保持AdaBoost算法精度的基础上,提出了一套浮点数转定点数的代码优化方案。经过大量实验,对比其相关领域研究成果,该系统具有在黑夜或光线变化强烈的情况下依然保持检测精度,可靠性高、实时性好的特点,驾驶员疲劳检测系统能够在实际的交通运输中应用。 展开更多
关键词 疲劳驾驶 人脸检测 PERCLOS算法 DSP 优化
HX_D2型电力机车MPU故障时无法正常冗余切换分析与改进
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作者 李佳 《铁道机车与动车》 2019年第2期17-19,38,5-6共5页
针对HX_D2型电力机车主处理单元MPU故障时备用MPU无法正常工作,分析了故障原因并提出改进措施,同时优化冗余切换逻辑,提高机车运行可靠性。
关键词 电力机车 主处理单元 冗余 优化
果树专用饼状缓释肥递肥机构设计与优化 预览
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作者 王富 王金星 +2 位作者 刘双喜 荆林龙 李友永 《农机化研究》 北大核心 2019年第2期85-90,97共7页
针对现代果园生产过程中出现的化肥施用不当、浪费资源、污染环境等问题,提出果园缓释肥施肥方法。缓释肥料由于肥力释放速率、方式和持续时间已知并可控制而被推广,发展潜力大,但目前缓释肥施肥以手工施肥为主,劳动强度大,作业效率低... 针对现代果园生产过程中出现的化肥施用不当、浪费资源、污染环境等问题,提出果园缓释肥施肥方法。缓释肥料由于肥力释放速率、方式和持续时间已知并可控制而被推广,发展潜力大,但目前缓释肥施肥以手工施肥为主,劳动强度大,作业效率低。为解决这些问题,提出了一种缓释肥施肥机凸轮递肥机构。该机构由单片机、步进电机、凸轮、推杆及递肥盒构成,实现可控单一递肥。基于碰撞函数接触算法(IMPACT-Function-based Contact)对不同基圆半径R的凸轮进行仿真,通过对凸轮递肥机构的仿真试验,获取凸轮机构与推杆机构之间的接触力数据及推杆运动时的加速度和速度数据。通过对所得数据进行分析,优化凸轮基圆半径,结果表明:当基圆半径为105mm时,不仅可以减少材料成本,而且凸轮与推杆之间的挤压与接触力、推杆运动的加速度和速度在要求范围内实现多目标最优。 展开更多
关键词 果树 缓释肥施肥机 优化
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响应面法优化戊聚糖曲奇饼干工艺配方研究
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作者 张强 赵卉珉 梁进 《粮油食品科技》 2019年第1期24-29,共6页
旨在改良传统曲奇饼干的制作工艺,研究了添加戊聚糖制备曲奇饼干的最佳工艺配方。在单因素实验明确戊聚糖粉、绵白糖以及烘焙时间对曲奇饼干感官品质影响的基础上,利用Box-Behnken设计实验,对产品进行品质分析。结果表明,采用戊聚糖粉... 旨在改良传统曲奇饼干的制作工艺,研究了添加戊聚糖制备曲奇饼干的最佳工艺配方。在单因素实验明确戊聚糖粉、绵白糖以及烘焙时间对曲奇饼干感官品质影响的基础上,利用Box-Behnken设计实验,对产品进行品质分析。结果表明,采用戊聚糖粉添加量10.85%,绵白糖添加量27.58%,烘焙时间18.93min的条件,制作戊聚糖曲奇饼干,具有传统曲奇饼干酥松香甜的口感和滋味,还起到为人体补充戊聚糖膳食纤维的作用。 展开更多
关键词 戊聚糖 曲奇饼干 响应面法 优化
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基于UG与MatLab马铃薯挖掘机分离筛仿真与优化 预览
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作者 李建 王春光 +1 位作者 谢胜仕 邓伟刚 《农机化研究》 北大核心 2019年第11期41-46,51共7页
针对升运链—分离筛式马铃薯挖掘机工作时薯土筛分不彻底及马铃薯破损率高的缺陷,提出了一种优化方案。首先,采用理论分析与虚拟仿真技术结合的方式对分离筛的摆杆与筛条的长度进行研究,根据研究结果对摆杆与筛条长度做出修改;其次,采... 针对升运链—分离筛式马铃薯挖掘机工作时薯土筛分不彻底及马铃薯破损率高的缺陷,提出了一种优化方案。首先,采用理论分析与虚拟仿真技术结合的方式对分离筛的摆杆与筛条的长度进行研究,根据研究结果对摆杆与筛条长度做出修改;其次,采用优化设计的方法寻找目标函数,确定影响目标的边界条件,从中寻找最佳方案对马铃薯挖掘机分离筛进行改进。结果表明:适当的缩短分离筛筛条的长度,加长分离筛后摆杆的长度有利于降低马铃薯的损伤率、提高分离率,最终优化确定摆杆的长度为420mm,二级分离筛筛条长度为495 mm。 展开更多
关键词 马铃薯挖掘机 分离加速度 优化
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溢洪道出口挑流鼻坎体型优化设计研究 预览
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作者 吴亮 牧振伟 《红水河》 2019年第1期1-4,共4页
KLBL水电站溢洪道出口挑流鼻坎,原设计方案采用单曲挑坎形式,经试验验证下泄流量不满足设计过流能力,严重影响了溢洪道的安全运行。笔者在理论计算的基础上,结合水工模型试验,对溢洪道挑坎体型进行了改进优化,提出采用双曲扩散挑流鼻坎... KLBL水电站溢洪道出口挑流鼻坎,原设计方案采用单曲挑坎形式,经试验验证下泄流量不满足设计过流能力,严重影响了溢洪道的安全运行。笔者在理论计算的基础上,结合水工模型试验,对溢洪道挑坎体型进行了改进优化,提出采用双曲扩散挑流鼻坎体型,通过模型试验分析,说明设计合理。 展开更多
关键词 溢洪道 挑流消能 挑流鼻坎 模型试验 体型 优化
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正交试验法优选康力宝胶囊提取工艺 预览
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作者 袁洪泽 杨瀚春 +2 位作者 夏鹏霄 贾文超 黄居兴 《海峡药学》 2019年第2期26-28,共3页
目的优选并确立康力宝胶囊的最佳提取工艺。方法采用水提工艺,以干浸膏量、松果菊苷和毛蕊花糖苷含量为指标,通过L9(34)正交试验对康力宝胶囊的水提工艺进行优化;建立了康力宝胶囊中松果菊苷和毛蕊花糖苷的含量测定方法。结果康力宝胶... 目的优选并确立康力宝胶囊的最佳提取工艺。方法采用水提工艺,以干浸膏量、松果菊苷和毛蕊花糖苷含量为指标,通过L9(34)正交试验对康力宝胶囊的水提工艺进行优化;建立了康力宝胶囊中松果菊苷和毛蕊花糖苷的含量测定方法。结果康力宝胶囊水提最佳工艺条件为A3B2C2,即加水量分别为12倍、10倍量,提取时间分别为90min,60min。结论优选出的水提工艺经济,方法简单可行,提取率较高。 展开更多
关键词 正交试验法 康力宝胶囊 提取工艺 优选
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