基于模糊多模型的堆芯功率控制

传统的堆芯功率PID控制器是基于单一功率水平处的堆芯局部模型设计的，难以准确描述整个堆芯功率水平范围的控制。因此，本文基于5个不同功率水平下的传递函数模型，通过三角隶属度函数加权，建立堆芯模糊多模型，并依据该模型设计堆芯功率模糊PID控制。以TMI型压水堆堆芯为对象，开展不同初始功率水平下的堆芯功率跟踪、堆芯进口温度扰动的控制仿真。结果表明，基于模糊多模型设计的堆芯功率模糊PID控制器可实现对堆芯功率的良好控制。     

Models to predict milk fat concentration and yield of lactating dairy cows: A meta-analysis

Few models have attempted to predict total milk fat because of its high variation among and within herds. The objective of this meta-analysis was to develop models to predict milk fat concentration and yield of lactating dairy cows. Data from 158 studies consisting of 658 treatments from 2,843 animals were used. Data from several feed databases were used to calculate dietary nutrients when dietary nutrient composition was not reported. Digested intake (DI, g/d) of each fatty acid (FA; C12:0, C14:0, C16:0, C16:1, C18:0, C18:1 cis, C18:1 trans C18:2, C18:3) and absorbed amounts (g/d) of each AA (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, Val) were calculated and used as candidate variables in the models. A multi-model inference method was used to fit a large set of mixed models with study as the random effect, and the best models were selected based on Akaike's information criterion corrected for sample size and evaluated further. Observed milk fat concentration (MFC) ranged from 2.26 to 4.78%, and milk fat yield (MFY) ranged from 0.488 to 1.787 kg/d among studies. Dietary levels of forage, starch, and total FA (dry matter basis) averaged 50.8 ± 10.3% (mean ± standard deviation), 27.5 ± 7.0%, and 3.4 ± 1.3%, respectively. The MFC was positively correlated with dietary forage (0.294) and negatively associated with dietary starch (?0.286). The DI of C18:2 (g/d) was more negatively correlated with MFC (?0.313) than that of the other FA. The best variables for predicting MFC were days in milk, FA-free dry matter intake, forage, starch, DI of C18:2, DI of C18:3, and absorbed Met, His, and Trp. The best predictor variables for MFY were FA-free dry matter intake, days in milk, absorbed Met and Ile, and intakes of digested C16:0 and C18:3. This model had a root mean square error of 14.1% and concordance correlation coefficient of 0.81. Surprisingly, DI of C18:3 was positively related to milk fat, and this relationship was consistently observed among models. The models developed can be used as a practical tool for predicting milk fat of dairy cows, while recognizing that additional factors are likely to also affect fat yield.     

自适应PID控制与传统PID控制的无抖振切换

提出一个自适应控制器和一个传统PID控制器相互切换的控制方案,仿真结果表明,自适应PID控制方法在辨识精度、控制性能和跟踪能力等问题上可以和多模型自适应控制相媲美,从而省去了多模型自适应控制寻找大量合适模型集的不易,是一种简便有效的控制方法.     

高温微波冶金反应器的研究现状及发展趋势

在简述微波加热基本原理的基础上,评述高温微波冶金反应器近年来的国内外研究现状,分析目前高温微波冶金反应器存在的主要问题,指出由于高温条件下微波系统的连续稳定性、适用于微波场的大尺寸高温反应内腔及高效的反应工程设计等难题,除小规模微波设备外,能够满足高温、高效、大功率的微波冶金反应器仍是空白,解决高温微波冶金反应器工业化应用在微波高温陶瓷材料、大功率微波发生器和物料温度测试等方面存在的主要问题,提高微波能的转换效率,研制、设计连续、稳定的大功率高温微波冶金反应器是微波工业化实践的关键。     

多模式协同人才培养体系改革与实践

本文以我校电子信息工程专业本科人才培养为例,建立了以电子信息工程专业为基础的协同式多模式应用型本科人才培养体系。我们针对多种培养模式,积极探索并实践了课程体系改革,初步形成了适应区域经济社会发展的协同式人才培养新模式。     

A multiple model approach for predictive control of indoor thermal environment with high resolution

Realtime and precise control strategies for indoor micro-climate are increasingly needed due to the requirements of thermal comfort and energy consumption saving in recent years. In this paper, a precise control scheme with high resolution is proposed for indoor thermal regulation. Finite volume method discretization/linearization are employed to construct the state-space model of the thermal process. Proper orthogonal decomposition method is further applied for model order reduction. On this basis, a multiple model approach is used to treat the thermodynamic coupling effect and a multi-model switching model predictive control (MPC) scheme is proposed for temperature tracking. Two cases including cooling and warming scenarios are designed, respectively, for performance validation. Results show that compared with the classic MPC method, the developed method can alleviate the model mismatch problem, and facilitate an optimal control of the spatial temperature in the considered ventilation room.     

基于动态MPLS的发酵过程效益函数在线预报

针对发酵过程观测数据与时序相关,提出一种动态多方向偏最小二乘回归(MPLS)方法,该方法的多模型结构解决了非线性和实时性的问题,更加适用于发酵过程的在线预报,与人工神经网络(ANN)方法相比,动态MPLS回归模型可以达到更好的拟合和预报精度.对青霉素发酵过程的效益函数拟在线预报,验证了该方法的准确性和有效性.     

一种基于D-S 和ARIMA 的多模型软测量方法

针对传统软测量方法存在的预测性能差、融合能力低等缺点, 提出一种基于证据理论(D-S) 合成规则和差分自回归滑动平均(ARIMA) 模型的多模型软测量方法. 首先利用自适应模糊核聚类方法和最小二乘支持向量机建立多个子模型; 然后利用D-S 合成规则构造的概率分配函数作为权值因子, 对子模型输出进行融合以得到多模型的输出; 最后结合ARIMA 模型对静态多模型输出进行动态校正. 仿真研究与工业应用的结果表明, 所提出的方法具有良好的预测性能和融合能力.

     

基于状态变量-多模型的主汽温控制系统

在现代火力发电厂控制中,锅炉出口过热蒸汽温度(主汽温)是锅炉的主要参数之一,对电厂的安全经济运行有着重大的影响。针对主汽温系统大惯性、非线性的特点,提出了基于状态变量-多模型的控制方案,即先采用状态反馈补偿主汽温的惯性,以PID作为控制器对补偿后的广义被控对象进行控制;再针对不同工况下对象特性的变化,对各工况控制子系统进行模态综合,实现大范围的控制。对主汽温系统的仿真结果表明,该方案可以较好地解决系统的大惯性和非线性问题。     

A multi-model approach to determine early manufacturing parameters for small-data-set prediction

Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause the model training to be overfitted. To address this issue, this paper presents an approach that combines multiple prediction models to extract data information in multiple facets. In the multi-model approach, a compromise weight method is proposed to determine the relative reliability of each of the prediction model. The methods used include multiple regression, artificial neural network, and support vector machines for regression. A thin-film transistor liquid crystal display manufacturing case study is used to illustrate the details of this research. The empirical results not only show that the proposed multi-model can reduce the manufacturing variation and increase the production yield, but also can propose a robust and reliable parameter interval to the online engineers in the early manufacturing stage.