智能建筑火灾探测技术的研究

本文阐述了智能建筑火灾探测技术的发展过程,就智能建筑物准确探知火灾发生的方法,以及复合探测器、模糊神经网络等技术在探测系统中应用问题进行探讨,以提高火灾探测信息的准确率,保障楼宇内人员、财物的安全。     

模糊神经网络的理论发展与应用研究

本论文主要介绍了模糊神经网络的产生与发展过程,模糊神经网络的特点与结构,并结合实例对其在解决问题中的应用进行了说明。     

基于B样条函数的模糊神经网络

通过研究Ｂ样条函数在模糊系统和神经网络系统中的应用，探讨了通过Ｂ样条函数，结合模糊系统和神经网络的各自特点，构造模糊神经网络的方法，并提出了具体的网络模型以及相应的学习方法。     

声呐目标跟踪关联中的模糊关联方法

声呐目标跟踪关联是为了获得精确的状态和属性估计、完全和实时的态势和威胁评估。在多台声呐系统联合探测中,根据相同目标、不同观测具有相似特征,提出了声呐目标模糊关联算法。模糊关联能够处理模糊性和不确定性信息,求解具有相似特征目标的关联值。通过将探测目标的方位数据模糊化,利用观测的模糊量和模糊隶属度函数进行数据关联,计算关联时长内两台声呐探测目标方位的关联值,仿真分析验证了上述算法的有效性并取得了较好的实验效果。     

基于区间值描述的自适应直觉模糊神经网络

针对模糊集合在语义描述上存在的不足,为提高自适应模糊神经网络的紧凑性,提出了自适应直觉模糊神经网络。首先,推导了适合神经网络计算的直觉模糊规则。接着,给出了自适应直觉模糊神经网络的结构和各层的含义,并给出了网络学习算法和收敛性分析。最后,通过典型实例仿真试验,表明提出的自适应模糊神经网络结构更为紧凑,学习算法的泛化性能更佳。     

神经网络-模糊推理数据融合技术应用研究

近几年来,神经网络与模糊推理技术相辅相成构成了比较完备的智能信息系统框架.本文以神经网络-模糊推理数据融合技术为主线,重点介绍模糊神经多传感器数据融合系统的建模与分析;针对C3I数据融合系统中,传感器受外界复杂环境的影响使得其探测到的传感器信息具有不确定性,通过将模糊技术、神经网络理论与 Petri网相结合,讨论了模糊神经多传感器数据融合系统的建模方法,这对于提高系统学习能力和对外界环境的自适应能力具有实际意义.     

基于模糊神经网络的决策支持系统的应用研究

论述了模糊神经网络的概念及特点，在此基础上提出了基于模糊神经网络的决策支持模型，还介绍了其运行计算的过程与方法，并通过农机选型配套的决策支持实例验证模型，说明模型对复杂的决策问题具有简单方便及快速准确做出反应的优点。     

基于多特征融合的烟雾检测

针对传统火灾探测技术的应用弱点,研究基于视频的火灾烟雾探测方法.首先,根据烟雾的颜色特征,提取视频序列中的疑烟区域.然后,在疑烟区域中提取烟雾的3个动态特征——扩散特征、轮廓不规则特征和使背景模糊特征.最后,利用BP神经网络对这些动态特征进行融合判定.实验结果表明,基于多特征融合的烟雾检测方法能够准确、实时、有效地识别视频中的烟雾.     

模糊聚类分析在模糊神经网络结构优化中的应用

研究了模糊聚类分析在多变量模糊神经网络的结构确定中的应用,在传统的模糊C－均值算法的基础上,给出了一个衡量聚类有效性的函数和确定模糊指数的启发式方法,并给出了应用该算法的具体的模糊神经网络模型。     

模糊神经网络在高层建筑横风向振动控制中的应用研究

提出了模糊神经网络方法控制高层建筑横风向风振反应。通过观测部分楼层加速度和控制力输出，建立了模糊神经网络控制器，解决了传统控制中有限的传感器数目对系统振动状态估计的困难．利用模糊神经网络控制器预测结构的控制行为，消除了闭环控制系统中存在的时滞。利用模糊神经网络控制器的自学习能力来确定模糊规则和语言变量隶属函数，解决了土木工程复杂结构模糊控制中，难于依据专家的主观经验来确定模糊控制规则和语言变量隶属函数等困难。模糊神经网络方法的优势在于算法自身的鲁棒性，处理结构非线性、参数不确定性及时变等问题的能力。通过对基准建筑的刚度不确定性分析，讨论了模糊神经网络控制器的鲁棒性。仿真分析表明，模糊神经网络控制策略能有效地抑制高层建筑的横风向风振反应，控制效果略优于LQG控制，而拥有LQG控制不具备的诸多优点。     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上:在这个子空间上用改进的BP算法训练进行逼近.实验结果表明:通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求.     

基于人工智能方法的地下洞室群爆破振动速度预测

特大断面地下洞库爆破开挖工程中涉及到众多的影响因素,为了较准确地预测出爆破振动速度,引入支持向量机理论,建立最小二成支持向量机爆破振动速度预测模型(LS-SVM模型),该模型利用结构风险最小化来提高求解问题的速度和精度。采用该模型对某地下水封LPG洞库工程进行爆破振动速度预测,并与传统的萨道夫斯基回归公式模型(萨氏模型)和模糊神经网络模型(FNN模型)进行对比分析。分析结果表明:LS-SVM模型、FNN模型与萨氏模型的全局均方根相对误差RMSRE分别为4.68%、14.42%与19.33%;LS-SVM模型有14组数据满足预测模型泛化能力误差阀值(6%)的要求,而FNN模型与萨氏模型均不满足要求。因此LS-SVM模型在爆破振动速度预测中的预测性能和泛化能力均优于FNN模型及萨氏模型,可为多因素影响下类似工程爆破振动速度预测提供借鉴经验。     

Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame. Finally, the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine, and the recognition results were obtained. The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.     

运用模糊神经网络的汽车座椅舒适性评价

汽车座椅舒适性受到消费者的高度关注。由于复杂性和主观性等因素的影响,使得汽车座椅舒适度评价成为座椅制造企业和整车企业的一个技术难点。提出一种基于模糊神经网络的座椅舒适度评价模型,应用8个座椅压力分布参数和2个人体参数等对座椅舒适性进行综合评价。实例分析显示:与普通BP网络模型对比,模糊神经网络模型具有更好的预测效果。     

新型磁流变脂阻尼器对铁路连续梁桥地震响应模糊神经网络控制

为解决传统磁流变液阻尼器(Magneto Rheological Fluid Damper,MRFD)磁场利用率低及磁流变液沉降导致控制特性劣化难题,提出新型阻尼器—磁流变脂阻尼器(Magneto Rheological Grease Damper,MRGD)。采用神经网络(Neural Network,NN)对足尺MRGD动力特性进行辨识,通过将改进的限幅最优(Modified Clipped-Optimal,MCO)算法整合到模糊神经网络(Fuzzy Neral Network,FNN)理论来设计适合MRGD的FNN/MCO半主动控制策略,并构建SIMULINK仿真分析平台。以典型三跨铁路连续梁桥为工程背景,分别对未控制、FNN/MCO半主动控制及线性二次型高斯(Linear Quadratic Gaussian,LQG)主动控制下桥梁各项评价指标进行分析。结果表明,所提FNN/MCO半主动控制策略对桥梁地震响应控制效果明显优于LQG主动控制策略;FNN/MCO策略较LQG策略更利于控制装置性能发挥;FNN/MCO策略稳定性、鲁棒性均明显优于LQG策略。     

Structural parameters identification of uncertain multi-storey shear buildings using fuzzy neural network modelling

Present paper proposes a fuzzy neural network (FNN)-based modelling for the identification of structural parameters of uncertain multi-storey shear buildings. Here, the method is developed to identify uncertain structural mass, stiffness and damping matrices from the dynamic responses of the structure without any optimization processes that are generally used to solve inverse vibration problems. Uncertainty has been taken in term of fuzzy numbers. The governing equations of motion are first solved by the classical method to get responses of the consecutive stories. Further the governing equations of motion are modified based on relative responses of consecutive stories in such a way that the new set of equations can be implemented in a cluster of FNNs. As such the model starts solving the nth floor by FNN modelling to estimate the structural parameters. Subsequently, series of FNN models are used to estimate the parameters for (n ? 1)th storey to the first storey. One may note that single layer FNNs have been used for training for each cluster of the FNN such that the converged weights give the uncertain structural parameters. The initial weights in the FNN architecture are taken as the design parameters in uncertain (fuzzy) form. In order to validate the present model, various example problems of different multi-storey shear structures have been considered. Related results are incorporated in term of tables and graphs. Comparisons between theoretical and identified results are carried out and are found to be in good agreement.     

Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive

A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results.      

基于模糊神经网络的设备故障预测研究

对模糊神经网络技术进行了研究，提出了预测分析的模糊神经网络模型；建立了故障指标评定方法，利用预测算法运用参数历史故障指标对参数指标进行趋势预测，预测得到的参数指标可以根据专家诊断系统判据进行诊断，对未来设备的健康状况进行可信度较高的评估。经仿真结果验证，该算法预测精度较高，预测结果可信.     

爆破振动特征参量的粗糙集模糊神经网络预测

摘 要 爆破振动特征参量对爆破振动危害效应有重要影响。首次用粗糙集模糊神经网络方法对振幅、主频率及主频持续时间进行预测。首先介绍了粗糙集模糊神经网络的基本思想,其次,分析了印象爆破振动特征参量的主要因素,建立了基于粗糙集模糊神经网络的爆破振动特征参量预测模型;最后用某边坡开挖爆破中的振动观测指标对模型进行了训练,并对15组指标进行了测试。结果表明：粗糙集模糊神经网络预测模型能反映了影响因素与特征量之间的非线性关系,适用于爆破振动特征参量预测。一次预测1个指标的精度高于同时预测3个指标的精度。     

An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network

An interval type-2 fuzzy neural network (IT2FNN) is developed for the position control of a thetas-axis motion-control stage using a linear ultrasonic motor to confront the uncertainties of the motion-control stage. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part and a three-layer interval neural network as the consequent part. A general T2FNN is computationally intensive due to the complexity of reducing type 2 to type 1. Therefore an IT2FNN is adopted to simplify the computational process. Moreover, the developed IT2FNN combines the merits of an interval type-2 fuzzy logic system and a neural network. Furthermore, the parameter-learning of the IT2FNN, which is based on the supervised gradient decent method using a delta adaptation law, is performed on line. Experimental results show that the dynamic behaviours of the proposed IT2FNN control system are more effective and robust with regard to uncertainties than the type-1 FNN control system.