大学生体质健康评价模型及不同评分标准的相通性

为更准确、客观的测定每个大学生体质健康等级,利用分段拟合方法分别构建大学男、女生体质健康评价模型,并得到体质测试分及各项测试项目基本得分间的具体表达式．后又具体论析了模型求解过程中,所涉及四个测试项目均占0．25的权重值与《国家体质健康标准》中公布的权重值之间是相通的．     

社会主义核心价值观引领大众文化建设探析——马克思主义人学视角

科学构建社会主义核心价值引领大众文化建设方案,应准确把握社会主义核心价值观的科学内涵与精神实质;夯实社会主义核心价值观引领大众文化建设的马克思主义人学基础,科学把握三者的互动品质;从马克思主义人学视角重视社会主义核心价值观引领大众文化建设的功能和价值,促进人的本质的自由全面发展。     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

板料拉深成形过程中的变压边力技术

压边力是板料成形过程中的重要参数之一。在概述压边力在生产中的主要用途和意义的基础上，从压边力的加载模式、理论模型、实验研究变压边力技术与拉延筋、人工智能技术的交叉应用等方面，比较全面的综述了压边力技术的研究现状及存在问题，并探讨压边力技术的研究方向。     

基于遗传算法的Al-5%Cu合金电脉冲孕育处理参数优化

以实验为基础,利用神经网络和遗传算法优化Al-5%Cu合金的电脉冲孕育处理工艺参数。神经网络的输入参数为脉冲电压、脉冲时间和电脉冲孕育处理时熔体温度,输出参数是合金凝固组织的晶粒度。在神经网络训练的基础上,采用遗传算法优化神经网络的输入参数。结果表明,神经网络和遗传算法的组合建模获得了较好的优化结果。     

控制技术及模糊控制在冲天炉熔炼中的应用

介绍了国内外冲天炉熔炼控制技术的发展状况。在分析国内铸造业具体情况后，提出了采用人工智能控制技术中的模糊控制理论对冲天炉熔炼控制的可行性和发展方向。     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

基于平均熵的自适应人工蜂群算法

针对基本人工蜂群算法容易陷入局部最优和早熟等问题,提出一种改进的人工蜂群算法(ASABC)。利用平均熵机制初始化种群,增加种群的多样性,避免算法陷入早熟;同时,采用自适应调节邻域搜索步长的策略来提高算法的局部搜索能力,提升算法的计算精度;为了平衡算法的全局搜索能力和局部搜索能力,引入自适应比例选择策略来代替人工蜂群算法的适应度比例选择方法。对8个标准测试函数的仿真实验结果表明,与3种常见的智能优化方法相比,改进的算法具有显著的局部搜索能力和较快的收敛速度。     

基于当前最优解的分段搜索策略的人工蜂群算法

为了克服人工蜂群算法在求解函数优化问题中所存在的局部搜索能力差、收敛精度低的缺点,提出了一种基于当前最优解的分段搜索策略的人工蜂群算法。该算法中跟随蜂利用由全局当前最优解和个体当前最优解引导的局部搜索策略逐维进行变异,并采用基于“分段思想”的局部搜索策略对蜜源进行贪婪更新,以提高蜜源的更新效率,从而提高了人工蜂群算法的局部搜索能力。6个标准测试函数的仿真实验结果表明,与基本人工蜂群算法相比,改进后的人工蜂群算法在寻优精度和收敛速度上均有明显提高。