Designing fuzzy inference system based on improved gradient descent method

The distribution of sampling data influences completeness of rule base so that extrapolating missing rules is very difficult. Based on data mining, a self-learning method is developed for identifying fuzzy model and extrapolating missing rules, by means of confidence measure and the improved gradient descent method. The proposed approach can not only identify fuzzy model, update its parameters and determine optimal output fuzzy sets simultaneously, but also resolve the uncontrollable problem led by the regions that data do not cover. The simulation results show the effectiveness and accuracy of the proposed approach with the classical truck backer-upper control problem verifying.

EFFICIENT GRADIENT DESCENT METHOD OFRBF NEURAL ENTWORKS WITHADAPTIVE LEARNING RATE

A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by "award-punish" strategy. Detailed deduction of the algorithm applied to RBF networks is given. Simulation studies show that this algorithm can increase the rate of convergence and improve the performance of the gradient descent method.

A Stochastic Gradient Descent Method for Computational Design of Random Rough Surfaces in Solar Cells

In this work,we develop a stochastic gradient descent method for the computational optimal design of random rough surfaces in thin-film solar cells.We formulate the design problems as random PDE-constrained optimization problems and seek the optimal statistical parameters for the random surfaces.The optimizations at fixed frequency as well as at multiple frequencies and multiple incident angles are investigated.To evaluate the gradient of the objective function,we derive the shape derivatives for the interfaces and apply the adjoint state method to perform the computation.The stochastic gradient descent method evaluates the gradient of the objective function only at a few samples for each iteration,which reduces the computational cost significantly.Various numerical experiments are conducted to illustrate the efficiency of the method and significant increases of the absorptance for the optimal random structures.We also examine the convergence of the stochastic gradient descent algorithm theoretically and prove that the numerical method is convergent under certain assumptions for the random interfaces.

A Gradient Descent Method for Estimating the Markov Chain Choice Model

In this paper,we propose a gradient descent method to estimate the parameters in a Markov chain choice model.Particularly,we derive closed-form formula for the gradient of the log-likelihood function and show the convergence of the algorithm.Numerical experiments verify the efficiency of our approach by comparing with the expectation-maximization algorithm.We show that the similar result can be extended to a more general case that one does not have observation of the no-purchase data.

Numerical partitioning of components for four-modal sedimentary grain-size distribution based on gradient descent method

The gradient descent(GD)method is used to fit the measured data(i.e.,the laser grain-size distribution of the sediments)with a sum of four weighted lognormal functions.The method is calibrated by a series of ideal numerical experiments.The numerical results indicate that the GD method not only is easy to operate but also could effectively optimize the parameters of the fitting function with the error decreasing steadily.The method is applied to numerical partitioning of laser grain-size components of a series of Garzêloess samples and three bottom sedimentary samples of submarine turbidity currents modeled in an open channel laboratory flume.The overall fitting results are satisfactory.As a new approach of data fitting,the GD method could also be adapted to solve other optimization problems.

A modified three–term conjugate gradient method with sufficient descent property

A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.

A New Descent Nonlinear Conjugate Gradient Method for Unconstrained Optimization

In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization. The sufficient descent property holds without any line searches. We use some steplength technique which ensures the Zoutendijk condition to be held, this method is proved to be globally convergent. Finally, we improve it, and do further analysis.

A Descent Gradient Method and Its Global Convergence

Y Liu and C Storey(1992)proposed the famous LS conjugate gradient method which has good numerical results.However,the LS method has very weak convergence under the Wolfe-type line search.In this paper,we give a new descent gradient method based on the LS method.It can guarantee the sufficient descent property at each iteration and the global convergence under the strong Wolfe line search.Finally,we also present extensive preliminary numerical experiments to show the efficiency of the proposed method by comparing with the famous PRP^+method.

Three New Hybrid Conjugate Gradient Methods for Optimization

In this paper, three new hybrid nonlinear conjugate gradient methods are presented, which produce suf?cient descent search direction at every iteration. This property is independent of any line search or the convexity of the objective function used. Under suitable conditions, we prove that the proposed methods converge globally for general nonconvex functions. The numerical results show that all these three new hybrid methods are efficient for the given test problems.

基于优化快速搜索随机树算法的全局路径规划

为了改善传统快速搜索随机树(RRT)算法在全局路径规划中存在的平滑度差、具有潜在碰撞性等问题,提出了一种双重优化的RRT算法。在传统RRT算法基础上,引入自适应目标偏向策略以缩短采样时间,引入角度约束采样策略以适应车辆极限转角。得到初始路径后,建立二项优化函数(即降低路径曲率和远离障碍物),并将其作为基点进行梯度下降二次优化,生成可供车辆行驶、平滑性良好且碰撞概率低的路径,并进行仿真验证。结果表明:优化RRT算法相比于传统RRT算法、RRT-Connect算法和RRT算法,平均曲率分别降低了38.1%、36.4%和24.7%,曲率均方差分别降低了38.4%、38.4%和27.2%。

基于算力-能量全分布式在线共享的5G网络负荷管理策略

5G与边缘计算等信息基础设施海量部署造成运营商用电成本上升,需推动边缘网络与电网的能量互动以节能降本。现有研究重点关注边缘网络参与日前经济调度,未考虑可再生能源和网络流量双重随机性造成的网络能量供需不平衡问题。针对强随机环境下的网络负荷管理问题,提出面向虚拟化边缘网络的能量实时管理策略。首先,以网络用能成本最小化为目标,构建联合网络资源管理、储能充放电与能量共享模型。其次,针对未来网络信息未知无法直接求解的问题,提出基于随机对偶次梯度法的在线管理策略。然后,针对资源共享涉及运营商隐私问题,提出全分布式的计算资源与能量协同共享算法。最后,仿真验证表明,所提在线算法在无需先验知识的前提下有效减少了5G边缘网络的用能成本。

基于改进Yolov5模型的纱筒余纱量检测方法

为实现针织圆纬机纱架上纱筒余纱量的实时检测,提出一种深度学习与传统图像处理相结合的检测方法。通过优化Yolov5的主干网络并加入Shuffle-Attention注意力机制,利用改进后模型在图像中检测并框出纱筒位置;然后利用透视变换、均值偏移、canny轮廓检测、闭操作等处理获取纱筒内外圆轮廓,设计基于梯度下降的圆拟合算法,拟合纱筒内外圆的轮廓,得到纱筒的内外圆半径;最后结合小孔成像的原理完成纱筒余纱量的测量。结果表明:改进后的Yolov5模型在纱筒检测精度上达到99.5%,检测速度可达20帧/s,同时模型参数减少至3.255×106可检测的最小纱筒余纱量为3 mm,当纱筒余纱量小于3 mm后,将其视为空筒,进行延时更换。本文算法拟合圆所花费时间是传统霍夫圆检测算法的1/4左右,因此可满足针织车间的实际应用需求。

集中供热系统水力热力耦合特性辨识方法研究

集中供热系统的运行能效主要受其水力热力特性的影响,换热站作为连接热源、热网和热用户的枢纽,是分析系统水力热力特性的关键,站内设备的长期运行性能衰减会导致供热系统整体能耗升高、能效下降,因此有必要对站内设备的运行性能进行诊断.基于换热站实测运行数据,分别采用最小二乘法和梯度下降对站内主要设备包括换热器、循环泵和调节阀的性能曲线进行了辨识,从而构建了集中供热系统的水力热力特性计算方法,全面分析了某典型换热站主要设备性能衰减情况和能耗水平,对主要设备运行性能进行了评估,并对设备维护的经济性和碳减排效果进行了计算.结果表明:换热站内主要设备性能存在不同程度衰减,其中换热器传热系数由1.00 kW/(m^(2)·K)下降至0.64 kW/(m^(2)·K),循环泵标况扬程由22.4 m下降至17.3 m,调节阀流通能力由183.0 t/h下降至82.4 t/h,且阀门整体开度较低,以上问题导致了系统整体能耗的增加;为消除气候变化对集中供热系统能耗分析的影响,结合度日数和实际供热面积,对系统能耗进行评价,与标准年相比,测试年采暖季同期平均热耗和电耗分别增加了0.86 GJ/(104 m^(2)·℃·d)和2.38 kW·h/(104 m^(2)·℃·d);对主要设备进行清洗和更换后,集中供热系统采暖季诊断时间内的同期累计电耗可降低16.3%,全生命周期内CO_(2)排放量可减少128.5 t.

基于分数阶偏微分的数字图像滤波去噪仿真

受多种因素影响,数字图像会呈现出不同程度的噪声图斑,导致图像质量、清晰度降低,加大了图像分析、检测、分割等难度,为此,提出基于偏微分方程的图像自适应滤波去噪算法。区分图像噪声点和非噪声点,检测图像的边缘特征,提高算法对边缘的保留能力;构建分数阶偏微分方程模型,通过模型完成对图像的自适应滤波去噪处理。选取含有不同程度的噪声图像对所提方法展开实验测试,结果表明,所提方法可以有效去除图像中的冗余噪声,使图像整体质量得到大幅度提升。

基于梯度下降法的大房郢水库洪水预报模型参数率定及适用性研究

大房郢水库长期承担合肥市的防洪、供水任务,准确可靠地开展洪水预报对当地防汛抗旱工作具有重要意义。分别采用新安江模型和前期影响雨量(antecedent precipitation index,API)模型构建大房郢水库入库洪水预报模型,基于梯度下降法对模型参数进行优选,并以方案合格率作为评价指标,对2种模型在该水库的适用性进行分析。结果表明:新安江模型和API模型预报方案合格率均为90.7%,预报精度达到甲等;2种模型在不同预报项目中各有优势,对于洪峰和峰现时差,API模型预报效果更好,预报合格率分别为88.9%和100%,较新安江模型分别提高6.7%和5.9%;对于径流深,新安江模型预报效果更为理想,预报合格率可达94.4%,相对于API模型其提升幅度约13.3%。此外,API模型更适用于单式洪水预报,而新安江模型则更适用于复式洪水预报。研究成果可为提升大房郢水库入库洪水预报精度提供理论依据。

一种用于模块化CPC均衡充电的最优化策略

随着锂电池在工业及生活中的广泛应用,锂电池组均衡充电策略的高效性及快速性也变得愈发重要,因此构建单体电池与电池模块间CPC(cell-to-pack-to-cell)的均衡充电系统,以解决锂电池组快速均衡充电问题。首先,对均衡系统进行模块化处理,建立模块内和模块间的均衡电路;然后,提出1种用于均衡充电的最优化策略,对所提策略模型进行分层求解,顶层通过二分法来计算充电时间,底层通过梯度下降法来优化充电电流;最后,通过与非模块化均衡充电策略下的充电时间、均衡时间、单体电池端电压及均衡器承担电压进行仿真对比,验证了所提策略的可行性和有效性。

基于BP算法的装配式建筑钢结构表观病害检测

装配式建筑钢结构表观病害检测过程中,受环境和主观因素影响的细小病害检测性能较低。因此,利用BP算法,设计一种新的装配式建筑钢结构表观病害检测方法。在检测车上搭载工业相机,采集装配式建筑钢结构图像,通过线性灰度拉伸、非线性滤波等方式,进行图像预处理。通过图像灰度共生矩阵,计算出图像纹理特征参数。依托于BP算法,构建包含数个3层神经子网络的表观病害检测模型,并通过自适应调整和训练,实现复杂环境下的装配式建筑钢结构表观病害准确检测。实验结果表明:所提方法输出检测结果的综合指数F1值在0.9以上,满足表观病害检测质量要求,优化了检测质量。

非对称弦测法中基于梯度下降的轨道不平顺复原方法

本研究提出了一种基于梯度下降的轨道不平顺复原方法。基于非对称弦测法理论,轨道不平顺的复原过程可描述为一个欠定线性系统的优化问题。本文提出采用梯度下降法求解该优化问题,采用了考虑正则项的二次型代价函数。为了评估所提出方法的性能,本研究搭建了一个轨道检测小车,并在一个缩尺桥梁模型上进行了现场试验。通过与水准仪测量值的比较,验证了基于梯度下降轨道不平顺复原方法具有良好精度。与传统的需要专门设计逆滤波器的轨道不平顺复原方法相比,本研究所提出的方法物理意义明确,只需基于非对称弦测法原理建立优化模型,这表明本方法在现场实测中具有良好的可操作性。此外,重复性评估表明,在调整不同的弦长以及多次重复测量时,本研究所提出的方法可复现一致的轨道不平顺结果,表明该方法具有良好的测量重复性。本研究所提出的方法可对轨道不平顺进行准确复原,为轨道平顺性状态及时有效评估提供数据支撑,进而保证列车运行的平稳与安全。

基于Daubechies小波尺度函数的炉膛空气动力场声学层析重建方法

声学层析测量是一种先进、有前景的炉内空气动力场非接触重建方法,其中可靠、优化的重建模型算法是关键。小波尺度函数包含所确定尺度以下所有信号频率信息,便于有效重构包含复杂频率成分的场对象,鉴于此,该文提出以小波尺度函数为基对待测空间场层析重建的方法。其采用具有紧支撑、正交特点的Daubechies小波以获得更好的场重建辨识度;对几何参数进行归一化处理以适应不同炉膛断面尺寸的场重建;采用梯度下降法求解线性方程组,得到稳定可靠的结果。通过构建不同典型仿真断面流场并重建,该方法均得到与原设定场良好的一致性,从而证明所提出场重建方法的有效性与可靠性。

基于自动微分的桁架结构材料非线性分析

基于深度学习技术,提出一种基于自动微分技术的桁架结构材料非线性问题求解方案。以各杆件的位移参数作为优化变量,采用随机算法初始化杆件位移,并对结构荷载分段。在每个荷载段中,先通过几何关系由位移参数求得杆件的应变,并由材料的本构关系计算出各杆件的应力,由节点力平衡方程构建损失函数。为了使得损失函数最小,再结合自动微分技术,构建损失函数与节点位移的计算图,以快速求解损失函数关于结点位移的梯度。最后,根据梯度下降法对杆件位移进行迭代优化,直至满足收敛条件。以桁架结构作为研究对象,基于线性强化模型,求解了不同桁架的材料非线性问题,并将计算结果与有限元解进行对比。结果表明,此方案在弹塑性问题分析中的可行性,且求解结果具备较高的精度。