基于DBN网络与BP神经网络PID控制的永磁同步电机调速策略比较研究

针对BP神经网络自身存在的学习速率固定、记忆不稳定等缺点,设计了一种基于DBN网络PID的永磁同步电机控制器,通过Matlab/Simulink对基于BP神经网络PID控制器的电机调速策略和基于DBN网络PID控制器的电机调速策略进行建模仿真分析,探讨两者对于PMSM调速策略中控制鲁棒性和稳定性的优劣。仿真结果表明,基于DBN网络PID的永磁同步电机调速控制策略训练效果更佳,具有更好的稳定性和鲁棒性。     

RBM20-Mediated Pre-mRNA Splicing Has Muscle-Specificity and Differential Hormonal Responses between Muscles and in Muscle Cell Cultures

Pre-mRNA splicing plays an important role in muscle function and diseases. The RNA binding motif 20 (RBM20) is a splicing factor that is predominantly expressed in muscle tissues and primarily regulates pre-mRNA splicing of Ttn, encoding a giant muscle protein titin that is responsible for muscle function and diseases. RBM20-mediated Ttn splicing has been mostly studied in heart muscle, but not in skeletal muscle. In this study, we investigated splicing specificity in different muscle types in Rbm20 knockout rats and hormonal effects on RBM20-mediated splicing both in cellulo and in vivo studies. The results revealed that RBM20 is differentially expressed across muscles and RBM20-mediated splicing is muscle-type specific. In the presence of RBM20, Ttn splicing responds to hormones in a muscle-type dependent manner, while in the absence of RBM20, Ttn splicing is not affected by hormones. In differentiated and undifferentiated C2C12 cells, RBM20-mediated splicing in response to hormonal effects is mainly through genomic signaling pathway. The knowledge gained from this study may help further understand muscle-specific gene splicing in response to hormone stimuli in different muscle types.     

The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling

Face milling is a process predominantly affected by dynamic variation of cutting forces, thermo-mechanical shocks and vibration leading to catastrophic tool failure along with gradual wear of the inserts. Keeping in view the industrial importance of this process, it is necessary to devise suitable methods to predict in advance the onset of tool failure without grossly impairing the machining set-up and the job. Hence, the applicability of back propagation neural network with delta bar delta learning rule for faster convergence has been studied with the above objective. The multi sensor based tool condition monitoring strategy shows that the learning rate adaptation scheme combined with the selection of suitable process parameters drastically reduces the training time of the artificial neural network without dispensing with the prediction accuracy.     

Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends

In this paper, an approach for developing the prediction model for polymer blends using a back-propagation neural network (BPNN) combined with the Taguchi quality method is presented in an attempt to improve the deficiencies in current neural networks associated with the design of network architecture, including the selection of one optimal set of learning parameters to accomplish faster convergence during training and the desired accuracy during the recall step. The objective of the prediction model is to explore the relationships between the control factor levels and surface roughness in the film coating process. In addition, the feasibility of adopting this approach is demonstrated in the study optimizing the learning parameters of the BPNN structure to forecast the target characteristics of the product or process with various control conditions in the manufacturing system.     

RBM学习方法对比

随着深度学习在模型、算法与理论上的突破性进展,以玻尔兹曼机为基础的各类深度模型近年来在目标识别与自然语言处理等诸多领域得到广泛应用。概述了玻尔兹曼机的相关概念,分析了受限玻尔兹曼机模型所具有的优势。对RBM中的学习方法进行了详细的描述,对应用最为广泛的受限玻尔兹曼机的几种典型学习算法进行了对比,并指出学习算法的研究在未来仍将是深度学习中的一项核心问题。     

控冷工艺参数对中厚板均匀冷却的影响

 确定了中厚板ACC冷却系统的换热边界条件,建立了钢板温度场有限元计算模型,利用现场实测数据对模型计算结果进行了验证,并且分析了不同冷却工艺参数包括集管开启方式、辊道速度以及冷却介质温度对钢板终冷温度、返红温度以及温差的影响规律,从而为实际生产中提高钢板控冷过程中的冷却均匀程度提供理论依据。     

双辊铸轧工艺温度场和流场耦合的数值模拟

 双辊铸轧过程中熔池内金属的流动状态及温度分布直接影响着铸轧过程的稳定性与铸带产品的质量。针对实验室双辊铸轧试验的特点,采用三维有限元法模拟了双辊铸轧过程的热流耦合问题,利用热平衡计算、铸轧实验和模拟相结合的反向方法分段建立了凝固过程中凝壳与铸辊之间热传导系数与铸轧速度、熔池位置之间的关系模型,并分析不同工艺条件下熔池内凝固变化的情况。     

碳锰钢热变形行为及动态再结晶模型

 采用单道次热模拟实验分析了各变形参数(初始晶粒尺寸、变形速率、变形温度和变形量)对碳锰钢动态再结晶的影响。结果表明,当初始晶粒越细小、变形温度越高、变形速率越小时,越易发生动态再结晶,同时在能够发生动态再结晶的条件下,变形量越大,动态再结晶越充分。得到了发生动态再结晶时的形变激活能Qdef、临界变形量模型、动态再结晶百分数模型及动态再结晶晶粒尺寸模型。计算得出ε05模型的平均误差为395%,模型预测值与实验数据计算值符合较好。     

基于RBF神经网络的中厚板轧机的温降模型

 基于Matlab神经网络工具箱,采用改进的径向基函数(RBF)网络优化计算4200中厚板轧机的轧制温度。通过径向基层散布常数的人工调整以及神经元的自适应调整,提高了收敛速度,确定了最佳的网络结构形式。网络预测结果与在4200中厚板轧机上实测的轧件温度进行了对比,预测精度很高,表明了该网络模型的优越性。该模型可为以温度参数为主要控制对象实行自动化生产的4200轧机提供可靠的参数,也可为人工神经网络在其它自动控制方面的应用提供参考。     

拉伸载荷作用下AlN夹杂物对航空用超高强度钢中裂纹萌生的影响

 采用扫描电镜原位观测法,跟踪观察了航空用超高强度钢中长方型夹杂物导致裂纹萌生与扩展的微观行为。结果表明,拉伸载荷作用下,航空用超高强度钢中裂纹萌生的方式与夹杂物尺寸及夹杂物周围孔洞的大小有关。当夹杂物面积小于一定值时,无论夹杂物周围有无孔洞,裂纹均以夹杂物/基体界面开裂的方式萌生;当夹杂物面积大于一定值后,若夹杂物/基体界面基本完好,则裂纹易以夹杂物自身开裂的方式萌生;若夹杂物周围孔洞面积较大,则裂纹易以夹杂物/基体界面开裂的方式萌生。