阀门气动噪声的振声转化计算方法

采用振动与噪声转化的方法计算气体流经阀门产生的管内气动噪声。通过推导管壁振动与管内噪声的计算公式,建立了管壁振动加速度级与管内噪声级之间的转换损失数理模型,并在低频区域,通过修正的频率因子,扩展了转换损失适用的频率范围,实现了通过阀门管内气动噪声的无损伤预测。利用实验对计算模型和方法进行了验证,结果表明,预测总声级的最大误差为0.98%,在整个频域内大约有69.3%⁷8.3%的数据预测误差在±5 dB以内,因而具有较高的精度,为阀门气动噪声的计算和分析提供了新的方法。     

基于深度神经网络的镜片佩戴舒适度研究

为了客观评价镜片佩戴舒适度,研究设计眼动实验,利用眼动仪采集并分析眼动数据,提出"眼睑间距变动百分比"作为镜片佩戴舒适度评价指标之一,再结合其他传统眼动指标,设计了一种基于深度神经网络的镜片佩戴舒适度评价模型.结果 表明提出的眼睑间距变动百分比指标用于评价镜片佩戴舒适度具有良好的效果,最终构建的深度神经网络模型预测准确...     

基于模糊神经网络的短时交通流预测方法研究

为满足交通控制和诱导系统的实时性需求,减少交通拥挤状况,降低交通事故突发频率,需要对短时交通流进行预测。当前的短时交通流预测方法是采用K-近邻的非参数回归对其进行预测,预测过程中没有将预测模型中关键因素对交通流的影响进行详细的说明,导致预测结果不准确,存在短时交通流预测误差较大的问题。为此,提出一种基于模糊神经网络的短时交通流预测方法。该方法首先以历史短时交通流数据样本序列为基础,将提取的关联维数作为短时交通流的混沌特征量,然后以该特征量为依据,对短时交通流数据进行聚类,使相同的短时交通流聚合类样本比不同的交通流聚合类样本更为贴近,采用高斯过程回归对短时交通流预测模型进行建设,建设过程中利用差分方法对短时交通流预测序列进行平稳化操作之后,对短时交通流预测模型进行训练,将GPR模型引入至短时交通流预测过程中,得到交通流预测方差估计值,并确定交通流预测值置信区间,由此实现短时交通流的预测。由此实现短时交通流的预测。实验结果证明,所提方法可以准确地预测交通运输系统的实时状况,为车辆行驶的最佳路线进行了有效引导,减少了自然影响方面和人为因素对短时交通流预测结果的干扰,为交通部门对交通路况的控制管理提供了依据。     

基于深度神经网络的时空编码磁共振成像超分辨率重建方法

单扫描时空编码磁共振成像是一种新型超快速磁共振成像技术,它对磁场不均匀和化学位移伪影有较强的抵抗性,但是其固有的空间分辨率较低,因此通常需要进行超分辨率重建,以在不增加采样点数的情况下提高时空编码磁共振图像的空间分辨率.然而,现有的重建方法存在迭代求解时间长、重建结果有混叠伪影残留等问题.为此,本文提出了一种基于深度神经网络的单扫描时空编码磁共振成像超分辨率重建方法.该方法采用模拟样本训练深度神经网络,再利用训练好的网络模型对实际采样信号进行重建.数值模拟、水模和活体鼠脑的实验结果表明,该方法能快速重建出无残留混叠伪影、纹理信息清楚的超分辨率时空编码磁共振图像.适当增加训练样本数量以及在训练样本中加入适当的随机噪声水平,有助于改善重建效果.     

基于时变阈值过程神经网络的太阳黑子数预测

太阳黑子活动直接影响着外层空间环境的变化，为保证航天飞行任务的安全必须对其进行有效预测.为此，提出了一种基于时变阈值过程神经网络的时间序列预测模型.为简化模型的计算复杂度，开发了一种基于正交基函数展开的学习算法.文中分析了模型的泛函逼近能力，并以Mackey-Glass时间序列预测为例验证了所提模型及其学习算法的有效性.最后，将该预测模型用于太阳活动第23周太阳黑子数平滑月均值预测，取得了满意的结果，应用结果同时表明：所提预测方法与其他传统预测方法相比预测精度有所提高，具有一定的理论和实用价值. 关键词： 太阳黑子数 时变阈值过程神经网络 时间序列预测 泛函逼近     

Time series prediction using wavelet process neural network

In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Macke-Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.     

Determining the input dimension of a neural network for nonlinear time series prediction

Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it ,the paper pressents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Fimally,some validation examples and results are given.     

基于神经网络的航天光学遥感器在轨信噪比测试方法

提出一种基于神经网络的航天光学遥感器在轨信噪比的的测试方法。通过模拟得到了大量的包含有不同信噪比等级的遥感图像,并将其作为网络训练和测试的样本。通过对遥感图像进行分析,找到了分别与景物结构和噪声有关的特征向量,并将其作为神经网络的输入。在对大量样本图片进行训练后,可完成对由遥感器传输下来的任意一幅地面景物图像进行信噪比的测试,从而避免了传统方法对特定地面景物目标在成像测量中的诸多弊端,平均测量误差约为10%。     

基于改进教学优化算法的Hermite正交基神经网络混沌时间序列预测

针对传统预测模型对混沌时间序列预测精度低、收敛速度慢及模型结构复杂的问题, 提出了基于改进教学优化算法的Hermite正交基神经网络预测模型. 首先, 将自相关法和Cao方法相结合对混沌时间序列进行相空间重构, 以获得重构延迟时间向量; 其次, 以Hermite正交基函数为激励函数构成Hermite正交基神经网络, 作为预测模型; 最后, 将模型参数优化问题转化为多维空间上的函数优化问题, 利用改进教学优化算法对预测模型进行参数优化, 以建立预测模型并进行预测分析. 分别以Lorenz 系统和Liu系统为模型, 通过四阶Runge-Kutta法产生混沌时间序列作为仿真对象, 并进行单步及多步预测对比实验. 仿真结果表明, 与径向基函数神经网络、回声状态网络、最小二乘支持向量机及基于教学优化算法的Hermite正交基神经网络预测模型相比, 所提预测模型具有更高的预测精度、更快的收敛速度和更简单的模型结构, 验证了该模型的高效性, 便于推广和应用.     

基于模糊边界模块化神经网络的混沌时间序列预测

提出一种模糊边界模块化神经网络（FBMNN）的混沌时间序列预测方法,该方法先对混沌时间序列观测点重构的相空间进行模块化划分,划分点的选取由遗传算法自动寻优.然后定义一个模糊隶属度函数,在划分边界一侧按照一定的模糊隶属度设定模糊边界带,通过模糊化处理,解决了各模块划分点附近预测结果的跳跃问题.最后每一模块,及其模糊边界的样本点都对应一个递归神经网络进行训练,通过预测合成模块输出结果.该方法对三个混沌时间序列基准数据集Mackey-Glass,Lorenz,Henon进行实验,结果表明该方法有效地提高了混沌时间序列预测效果. 关键词： 模糊边界 模块化神经网络 混沌时间序列 预测     

Fuzzy neural network based on a Sigmoid chaotic neuron

The theories of intelligent information processing are urgently needed for the rapid development of modem science. In this paper, a novel fuzzy chaotic neural network, which is the combination of fuzzy logic system, artificial neural network system, and chaotic system, is proposed. We design its model structure which is based on the Sigmoid map, derive its mathematical model, and analyse its chaotic characteristics. Finally the relationship between the accuracy of map and the membership function is illustrated by simulation.     

Adaptive fuzzy neural network control on the acoustic field in a duct

Base on the principle of the superposition of waves, active noise control is achieved by adaptively tuning a secondary source which produces an anti-noise of equal amplitude and opposite phase with primary source. This paper presents the study on the acoustic attenuation in a duct by using the combination of fuzzy neural network with error back propagation algorithm to control secondary source. The most important advantage of fuzzy inference system is that the structured knowledge is represented in the form of fuzzy IF-THEN rules. But it lacks the ability to accommodate the change of external environments. Combining neural network with fuzzy system can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. The performance of attenuation and control error can be measured by the microphone placed in the downstream of duct. The results of this study, show that the acoustic attenuation by 40 dB for pure-tone noise and nearly 30 dB for dual-tones noise are obtained.     

基于径向基函数神经网络的未知模型混沌系统控制

基于径向基函数神经网络的智能方法对混沌进行控制-该方法不需要被控混沌系统的解析模型，控制的目标可以为周期轨道，也可以为连续变化的目标函数，在模型参数发生摄动和存在测量噪声情况下，控制仍然有效-研究了神经网络误差对控制精度的影响，并给出相关的定理及证明-针对Logistic映射和Henon吸引子的仿真结果，表明了此方法的有效性和可行性- 关键词： 混沌控制 径向基函数神经网络 参数摄动 测量噪声     

Revisiting the dynamics of Bose–Einstein condensates in a double well by deep learning with a hybrid network

Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences. In the present work, we propose a novel learning method based on a hybrid network integrating two different kinds of neural networks: Long Short-Term Memory (LSTM) and Deep Residual Network (ResNet), in order to overcome the difficulty met in numerically simulating strongly-oscillating dynamical evolutions of physical systems. By taking the dynamics of Bose–Einstein condensates in a double-well potential as an example, we show that our new method makes a highly efficient pre-learning and a high-fidelity prediction about the whole dynamics. This benefits from the advantage of the combination of the LSTM and the ResNet and is impossibly achieved with a single network in the case of direct learning. Our method can be applied for simulating complex cooperative dynamics in a system with fast multiplefrequency oscillations with the aid of auxiliary spectrum analysis.     

基于传感器网络的广域噪声监测技术

设计了一种针对城市环境噪声的广域噪声传感器网络系统,采用微型前置放大器和数字化网络采集仪组成检测系统,完成噪声信号的接收和采集,并通过软件设计完成采集的噪声信号存储、分析、显示及处理,实现了对噪声分布的图像化展示。     

圆柱绕流气动声的偶极子及四极子源法定量研究

以亚临界三维圆柱绕流的气动噪声为对象,研究声类比理论中偶极子及四极子源模型在预测低Mach数流动气动声的可靠性及准确性。使用大涡模拟(LES)得到非定常流场,并依据声类比中的Curle等效偶极子面源和Lighthill四极子体源模型,提取相应的声源数据,经Fourier变换得到涡脱落频率处的声源信息,进而定量预测圆柱绕流的气动声。结果表明:Curle模型的结果与实验结果吻合良好,Lighthill体源模型预测的准确性依赖于声源区域截断,不恰当的声源截断将导致错误的声场预测。     

Spatial coherence resonance induced by coloured noise and parameter diversity in a neuronal network

Spatial coherence resonance in a two-dimensional neuronal network induced by additive Gaussian coloured noise and parameter diversity is studied. We focus on the ability of additive Gaussian coloured noise and parameter diversity to extract a particular spatial frequency (wave number) of excitatory waves in the excitable medium of this network. We show that there exists an intermediate noise level of the coloured noise and a particular value of diversity, where a characteristic spatial frequency of the system comes forth. Hereby, it is verified that spatial coherence resonance occurs in the studied model. Furthermore, we show that the optimal noise intensity for spatial coherence resonance decays exponentially with respect to the noise correlation time. Some explanations of the observed nonlinear phenomena are also presented.     

Prediction of flow-generated noise produced by acoustic and aerodynamic interactions of multiple in-duct elements

Flow-generated noise problem caused by in-duct elements is due to the complicated acoustic and turbulent interactions of multiple in-duct flow noise sources. The approach of partially coherent sound fields used previously by Mak and Yang [C.M. Mak, J. Yang, Flow-generated noise radiated by the interaction of two strip spoilers in a low speed flow ducts, Acta Acust united with Acustica 88 (2002) 861–868] and Mak [C.M. Mak, A prediction method for aerodynamic sound produced by multiple elements in air ducts, J Sound Vib 287 (2005) 395–403] is adopted to formulate the sound powers produced by interactions of multiple elements at frequencies below and above the cut-on frequency of the lowest transverse duct mode. The study indicates that the level and spectral distribution of the additional acoustic energy produced by the interactions of multiple elements can be predicted based on the measured data with respect to the interactions. The proposed method can form a basis of a generalized prediction method for flow-generated noise produced by multiple elements. The application of the proposed method is supported by two engineering examples.     

基于数据融合的风电机组噪声预测

根据风电机组噪声信号检测复杂的情况,研究风电机组非声学参数的信息熵特征,对机组噪声进行多源数据融合预测。分析基于信息熵的非声学参数的特征提取方法,并对传统的基于遗传算法的支持向量机回归(GA-SVR)的缺陷提出改进,结合实际应用的非声学参数的信息熵特点平衡遗传算法(GA)的终止条件。通过统计分析完成了输入变量的筛选,去除了对预测影响较大的共线性因素,并实现了输入降维提高预测精度和速率。最后应用数据的信息熵特征,训练改进的GA-SVR建立最终的多源数据特征级融合预测模型。通过对比表明基于多源数据融合的预测方法精度最高,预测结果的相对误差平均值为0.7757%,具有实际可行性。