智能化无创血氧饱和度检测系统的研究

无创脉搏血氧饱和度仪能够快速实时进行血氧饱和度的测量，在临床中得到广泛的应用，针对复杂生物组织如何建立相对准确的数学模型是提高血氧仪精度的关键问题，本文分析了光通过人体组织的传输特性，采用辐射传输理论，并考虑散射效应，提出了一种新型无创脉搏轿氧仪的设计方案，有效地减少了血氧仪的测量误差，通过对PC机联网，实现了血氧测量的智能化。     

LISP跳棋程序与启发式搜索

本文叙述了在IBM-PC个人计算机上,研制成功的LISP跳棋智能程序中,如何成功地使用启发式搜索进行设计的方法,特别对作者自行设计的顺序递归搜索算法进行了分析和评价。     

神经网络理论在微分对策中的应用

阐述了微分对策理论在实际应用中存在的问题,并从动态冲突的实际过程出发,将神经网络理论与分层式智能控制理论相结合,来模型化微分对策问题.将微分对策的双向优化问题转化成基于神经网络理论的目标机动性辨识和单向优化控制问题,将微分对策的定性与定量结合起来,为有效加入人的经验提供了一种技术途径.并采用自适应分布式学习速率,大大提高了神经网络的学习速度和精度.     

航空燃气涡轮发动机气路故障诊断现状与展望

燃气涡轮发动机在航空、地面和舰船上有着广泛的应用,对其进行性能评估和故障诊断有着重大的现实意义。本文对发动机气路诊断的发展以及当前的主要气路诊断方法如故障方程法,基于非线性模型的诊断方法,基于人工智能等方法进行了综述,最后对航空发动机气路诊断的发展进行了展望。      

BP神经网络用于传感器故障诊断的仿真研究

在标准逆传播神经网络的基础上 ,提出了一种全新的改进逆传播神经网络 ,用来消除标准逆传播神经网络收敛速度慢和易陷入局部最小点的缺陷 ;并且设计了一个主神经网络和三个分布神经网络的结构 ,通过神经网络的在线学习 ,得到需要的参数估计。用它与传感器测得的实际值比较 ,可判断出故障 ,并且给出了仿真实例。     

基于人工智能方法的传感器故障诊断技术

在液体火箭发动机地面试验过程中,传感器的失效率远高于发动机部件、组件的故障率,因而提出了采用基于人工智能的方法对传感器的故障进行检测与诊断.本文对基于人工智能方法用于传感器故障检测与诊断的特点进行了分析,并分别应用BP神经网络和自适应神经模糊推理系统（ANFIS）对某次传感器的故障进行检测.结果证明基于人工智能的方法稳定可靠,具有良好的工程应用前景.     

智能化雷达对抗情报处理技术研究

随着雷达技术的迅猛发展,雷达信号的密度、复杂程度都大幅度提高,这就增加了雷达信号的不确定性因素。本文讨论了粗糙集理论、模糊集理论、证据理论和可能性理论等人工智能技术在雷达对抗情报处理中的应用。对雷达辐射源的型号和工作模式识别、雷达网信息以及所属平台信息的推理技术进行了探索研究,给出了研究建议。     

Advanced multiple response surface method of sensitivity analysis for turbine blisk reliability with multi-physics coupling

To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method(AMRSM) was proposed for multi-failure mode sensitivity analysis for reliability. The mathematical model of AMRSM was established and the basic principle of multi-failure mode sensitivity analysis for reliability with AMRSM was given. The important parameters of turbine blisk failures are obtained by the multi-failure mode sensitivity analysis of turbine blisk. Through the reliability sensitivity analyses of multiple failure modes(deformation, stress and strain) with the proposed method considering fluid–thermal–solid interaction, it is shown that the comprehensive reliability of turbine blisk is 0.9931 when the allowable deformation, stress and strain are3.7*10~(-3)m, 1.0023*10~9 Pa and 1.05*10~(-2)m/m, respectively; the main impact factors of turbine blisk failure are gas velocity, gas temperature and rotational speed. As demonstrated in the comparison of methods(Monte Carlo(MC) method, traditional response surface method(RSM), multiple response surface method(MRSM) and AMRSM), the proposed AMRSM improves computational efficiency with acceptable computational accuracy. The efforts of this study provide the AMRSM with high precision and efficiency for multi-failure mode reliability analysis, and offer a useful insight for the reliability optimization design of multi-failure mode structure.     

基于人工蜂群算法的航空发动机参数自整定PID控制

针对航空发动机PID控制器的参数寻优问题,提出基于人工蜂群算法(ABC)的航空发动机PID控制器参数自整定的方法。利用人工蜂群算法实现容易、计算简洁、寻优性能好、鲁棒性强的优点,优化PID控制器参数,以改善控制系统性能。考虑到可能存在的执行机构输出量的极大超调,融入执行机构输出的超调量惩罚机制,改进目标性能函数,有效避免了可能存在的发动机供油量极大超调现象。对涡扇发动机在地面和高空稳态工作点的增量形式的线性化数学模型进行了仿真验证,结果表明优化效果良好,转速阶跃响应反应迅速且超调量小,同时供油量也没有出现大的超调,验证了所提方法的有效性和实用性。     

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.