多级复合布尔函数性能研究

本文研究了加法复合布尔函数和乘法复合布尔函数,在平衡性、k次项系数、K次扩散准则(PC)、相关免疫 性(CI)、Walsh谱、代数次数、小项分布等方面得到了一批新结果。     

太阳电池板结构应力-应变状态分析

多层胶接是太阳电池板的结构特点,由于不同材料的弹性模量、热膨胀系数和泊松比不同,在温度场 作用下会产生热应力应变,在多次热交变过程中热应力．应变累积最后导致结构层间剥离,因此研究温度场作用 下太阳电池板结构应力应变状态具有非常重要的实际意义。本文推导了模拟太阳电池板结构应力-应变状态的一 维模型,该模型同样适用于分析多层胶接结构应力应变状态。     

数据挖掘预测模型在脑损伤患者认知功能康复中的应用

为了更好地预测后天性脑损伤ABI(Acquired Brain Injury)患者认知功能康复的影响因素,提出基于决策树(DT)、多层感知器(MLP)和广义回归神经网络(GRNN)的三种预测模型。借助于10折交叉验证测试算法,通过专一性、灵敏度和精度分析以及混淆矩阵分析对模型的性能进行测试,从而获得新的知识以评估和改善认知功能康复过程中的有效性。实验结果表明,基于DT的模型的模拟结果明显比其他模型更为优越,预测平均精度可高达90.38%。     

多层结构隐身涂层附着力测试探讨

优选了多层结构隐身涂层附着力的测定方法,研究了多层结构隐身涂层在自然环境中附着力的变化规律,指出了树脂耐低温和耐温变的重要性。     

利用模糊感知器解决模糊可分样本的分类问题

针对一类用于解决分类问题的模糊感知器,提出完全随机输入的模糊δ-规则,并给出训练样本模糊可分的定义。实例表明,利用该算法可以有效地解决模糊可分样本的分类问题,在有限步迭代后就达到收敛,即有限步训练后网络能将所有样本正确分类。     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

基于MLP和SPSO的机器人行为选择与运动控制方法

针对传统行为选择机制(ASM)不能很好地做出控制决策的问题,提出一种基于多层感知(MLP)前馈神经网络的ASM,并将其应用到移动机器人目标跟踪中。首先,根据具体应用场景预定义多个机器人行为。然后,根据机器人配备的图像和红外传感器获得的目标位置和障碍物信息,通过MLP神经网络从预定义行为中选择出所需执行的行为。另外,为了构造最优的MLP模型,采用一种简化粒子群算法(SPSO)来优化网络权值参数。机器人目标跟踪仿真的结果表明,提出的ASM能够准确选择出合适的行为,实现了控制机器人跟踪目标移动且能够避开各种障碍物。     

提高湿法烟气脱硫喷淋塔脱硫和除尘效率的探讨

分析了现代大型喷淋塔为提高脱硫与除尘效率在设计上所作的改进及存在的问题。通过一个曾经设计过的喷淋塔实例,结合烟气和吸收液两相逆向流动和两相间的化工传质过程,剖析了喷淋层覆盖率的实质,提出弱效喷淋覆盖和多重喷淋覆盖对喷淋塔脱硫和除尘效率的影响,给出喷淋覆盖率的选择、喷淋层和喷嘴布置的设计方法,对改善烟气脱硫和除尘效率均有明显效果。     

小口径钢管全位置脉冲TIG多层焊的组织分析

采用全位置脉冲TIG焊技术对小口径钢管环缝进行焊接，焊接采用TIG焊打底＋TIG填丝的多层焊工艺，分析了打底焊及每层填丝焊接完成后的打底层焊缝组织。结果表明，采用多层焊工艺可以很好地避免焊缝及热影响区的扩大，并且后焊层对前焊层的再加热作用，可细化晶粒，改善组织。     

Location of steel reinforcement in concrete using ground penetrating radar and neural networks

Ground-penetrating radar is becoming increasingly popular for use as a non-destructive assessment method for investigating reinforced concrete structures. The amount of data collected however can be very large and take a significant level of subjective experience to interpret. This study focuses upon the use of a neural network approach to automate and facilitate the post-processing of ground penetrating radar results. The radar data is reduced to a simplified data set by using an edge detection routine. Signal reflections from reinforcing bars displaying a hyperbolic image format are detected using a multi-layer perceptron (MLP) network with a single hidden layer containing 8 nodes to recognise a simplified hyperbolic shape. Training and testing of the network was carried out making use of an emulsion analogue tank, simulating the properties of concrete, and using real concrete specimens. The results showed that the use of a MLP neural network approach could be quite effective in automating the identification and location of embedded steel reinforcing bars from a radar investigation. Accurate estimation of depth, or cover, requires a reliable knowledge of the dielectric properties of the concrete, and recent work using a specially-developed wideband horn antenna for direct determination of in situ properties is also outlined.