平面交叉杆系变形协调普适方程及其通解

本文运用拓扑学、微分学、几何学的数学方法,捕捉到平面三交叉杆系的变形特点——拓扑特征.在小变形条件下,通过变换得到变形协调普适方程.从而推导出解此类拉压超静定问题的一种快速有效方法.该方法可直接应用于教学与工程计算.     

模糊方向神经网络在火箭发动机故障检测与分离中的应用

提出了一种模糊方向神经网络分类器,并应用于液体推进剂火箭发动机故障检测与分离。模糊方向神经网络采用模糊集表示发动机故障模式,模糊集是方向超体聚集形成的集合体,方向超体则由单位方向、夹角和两个半径确定。模糊方向神经网络能在一次循环学习中形成非线性方向边界。故障检测与分离的仿真研究表明：模糊方向神经网络的识别性能是比较优越的。     

通信系统接入的智能控制

针对通信系统在传统接入控制上的一些缺点,提出了模糊神经网络接入控制方法。首先,简单介绍了通信系统的接入控制原理,指出了传统接入控制的缺点;其次,描述了模糊神经网络模型,并在此基础上对网络进行优化;最后,同传统的接入控制方法进行比较。     

聚碳硅烷先驱体制备SiC基复合材料的性能研究

本文以聚碳硅烷(PCS)为先驱体,SiC晶须,SiC微粉或C纤维为增强剂,热解转化制得SiC/SiC或C/SiC复合材料,研究其制备工艺过程对材料的力学和热物理性能的影响。结果表明:PCS在1300℃下转化为β-SiC微晶并将未烧结的增强剂网络在一起形成SiC/SiC或C/SiC复合材料。该SiC基复合材料具有较好的常温和高温机械强度,优异的耐热疲劳和抗热震性能,在1300℃空气中具有良好的抗氧化性。     

联合作战系统效能评估

采用面向过程的系统分析方法建立了一个对联合作战系统效能比较精确的量化评估模型.模型以某个参考系统为标准,先作层次分析,然后通过模糊对比,把各层次中难以具体量化因素进行了量化,再通过判断矩阵得出各自权重,最后线性加权便可以得到总的系统相对于标准系统的优劣程度,是一种面向过程的系统分析.该方法适用面广,对复杂系统效能评估工作有一定指导意义,具有广阔的应用前景.     

武器系统模糊寿命周期费用模型

武器系统的寿命周期费用建模较多采用参数法,而参数法中最常用的是最小二乘回归.考虑费用统计数据的模糊性,提出用模糊最小二乘回归来建立武器系统模糊寿命周期费用模型,并结合实例对武器系统寿命周期费用进行了分析.结果表明,这种方法能达到令人满意的拟合精度,具有实用价值.     

基于免疫FNN算法的加热炉炉温优化控制

针对复杂钢坯加热过程,提出了一种免疫克隆进化模糊神经网络(ICE-FNN)控制算法。首先根据现场样本数据建立过程神经网络模型;然后基于该模型,采用模糊神经网络控制器(FNNC)规则优化算法,确定FNNC的最佳规则数;最后由FNNC的规则优化所得参数构造初始种群的一个解,采用免疫克隆进化(ICE)算法对FNNC参数优化。该算法具有全局寻优和局部求精能力,仿真结果证实了其有效性。     

干扰抑制类生成式对抗网络

为进一步改善超低频频段的通信质量,在传统改进广义旁瓣抵消算法的基础上,提出新的超低频干扰抑制算法——生成式旁瓣抵消算法。该算法将人工智能研究热点之一的生成式对抗网络模型引入广义旁瓣抵消算法中,通过优化设计生成模型的网络结构及相关超参数,有效地解决了原算法存在的期望信号残留问题,为旁瓣抵消通道中的后级滤波算法提供了与主通道相关性更强的干扰参考信息,从而提高了算法对主通道干扰估计的准确性。为了验证优化后生成模型的有效性以及所提算法对不同类别干扰的抑制能力,在实验室环境下搭建实验平台,设计了多组对照实验。实验结果表明:优化后的生成模型具有较好的生成能力、较好的鲁棒性以及相对较低的运算复杂度;相比于传统改进的广义旁瓣抵消算法,所提算法进一步提高了信号带宽内的信干噪比。     

Effect of nano powder (Al2O3-CaO) addition on the mechanical properties of the polymer blend matrix composite

This work describes the preparation and study of the properties of composite nanoparticles prepared by the sol-gel method which consists of two materials (Al₂O₃-CaO), and study the effect of these nanoparticles on the mechanical behavior of a polymer blend (EP 4% + 96% UPE). The powder was evaluated by X-ray diffraction analysis, scanning electron microscopy analysis (SEM), particle size analysis, and energy dispersive X-ray analysis (EDX). The mechanical behavior of the composite material was assessed by tensile test, bending test and hardness test. The evaluation results of the composite nanoparticles showed good distribution of the chemical composition between aluminum oxide and calcium oxide, smoothness in particles' size at calcination in high and low temperatures, formation of different shapes of nanoparticles and different (kappa and gamma) phases of the Al₂O₃ particles. The results of mechanical behavior tests showed marked improvement in the mechanical properties of the resulted composite material, especially at 1.5%, compared with polymer blend material without nano powder addition. The tensile properties improved about (24 and 14.9) % and bending resistance about (23.5 and 16.8) % and hardness by (25 and 22) % when adding particles of size (63.8 and 68.6) respectively. Therefore, this reflects the efficiency of the proposed method to manufacture the nanocomposite powder and the possibility of using this powder as a strengthening material for the composite materials and using these composite materials in bio applications, especially in the fabrication of artificial limbs.     

Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment

Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment, the evaluation information of alternatives given by decision makers is often fuzzy and uncertain. As a generalization of intuitionistic fuzzy set (IFSs) and Pythagoras fuzzy set (PFSs), q-rung orthopair fuzzy set (q-ROFS) is more suitable for expressing fuzzy and uncertain information. But, in actual multiple attribute decision making (MADM) problems, the weights of DMs and attributes are always completely unknown or partly known, to date, the maximizing deviation method is a good tool to deal with such issues. Thus, combine the q-ROFS and conventional maximizing deviation method, we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper. Firstly, we briefly introduce the basic concept of q-rung orthopair fuzzy sets (q-ROFSs) and q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs). Then, combine the maximizing deviation method with q-rung orthopair fuzzy information, we establish two new decision making models. On this basis, the proposed models are applied to MADM problems with q-rung orthopair fuzzy information. Compared with existing methods, the effectiveness and superiority of the new model are analyzed. This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers (q-ROFNs) and whose attributes are incomplete.