硬质合金刀片平面轮廓获取方法的研究

在硬质合金可转位刀片计算机辅助测量系统中，如何让计算机自动获取刀片的平面轮廓对测量控制有着重要意义。本文提出了两种方法：刀片型号法和图形交换法，并详细论述了实现的原理，定义了相应的数据结构。     

基于改进的小世界网络的组搜索算法及其应用

针对合成氨工艺中,合成塔出口氨含量软测量神经网络模型的参数优化问题,提出了一种改进的小世界网络模型,并将其引入到组搜索优化算法中。改进后的小世界网络在计算节点之间相连接的概率时考虑节点间的距离因素,相距较近的节点间比相距较远的节点之间更容易连接。将改进后的小世界网络引入组搜索算法后,种群中的追随者在每次迭代中,根据当前迭代次数及与其它个体间的距离计算自身与其它个体的相连接概率,相连概率随着迭代增加而线性增大,随着个体间距离的增加而指数递减。追随者根据相连概率选择与自身相连接的个体,再用与其相连的个体构建自身的邻域并选取邻域中的最优个体作为局部最优解个体。追随者更新自身位置时不仅参考整个种群中的全局最优个体,还同时参考自身邻域中的局部最优个体。该算法能够提高种群中个体的多样性,具有更好的全局搜索能力和收敛速度。将该算法应用于氨合成塔出口氨含量软测量建模问题中,对神经网络模型的参数进行优化,并与其它3种方法比较。仿真结果表明,基于改进的小世界网络模型的组搜索算法能够获得更好的模型参数,提高软测量模型的精度。     

基于改进的GSO算法和BP神经网络的氨合成塔出口氨含量软测量模型(英文)

The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.