Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

基于DFT-MARTE模型的时序分析算法

针对航天嵌入式软件(aerospace embedded software,AES)时序需求复杂带来的时序需求定义不准确问题,提出一种基于MARTE(modeling and analysis of real-time and embedded systems)模型的数据流时序(data flow timing based on MARTE,DFT-MARTE)模型,设计基于该模型的处理点缓存计算算法、时序偏离概率检测算法和时序序列分析算法。处理点缓存计算算法动态更新缓存空间,使后续时序检测正常执行;时序偏离概率检测算法利用多线程并发模拟时序特性,检测需求中时序偏离问题;时序序列分析算法是基于梯度下降算法,拟合时序序列,指导用户优化需求。该模型相比传统数据流模型更适用航天嵌入式软件,利于后续开发和维护,具有极高的应用价值。

基于PSO-BP的抗乳腺癌药物毒性研究

为解决新药研发过程中药物的毒性难以准确预估的问题,利用计算机技术,提出一种基于粒子群算法(PSO)优化BP神经网络的二分类预测模型。通过互信息的方法从729个分子描述符中筛选出重要度最高的20特征作为自变量,以药物的毒性值作为因变量,在BP神经网络模型的基础上,首先使用不同的梯度下降算法计算模型的准确率,发现批量梯度下降算法对BP模型的拟合效果较好;其次利用动态变权重的粒子群算法对BP神经网络模型的权重和阈值进行优化选择,结合BP神经网络、SVM和KNN模型进行对比实验,结果显示,PSO-BP模型的准确率、精确率、召回率和F1值明显高于其它模型。因此,PSO-BP模型是一种对抗乳腺癌药物毒性有效预测的方法。

Gradient Descent Algorithm for Small UAV Parameter Estimation System

A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent algorithm.Performance of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis turntable.The on-ground,turntable and flight experiments indicate that the estimation attitude has a good accuracy.Also,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.

基于改进Adam-DBN的油井工况诊断方法

针对深度信念网络在油井工况诊断中由于梯度扩散导致训练效果差,模型诊断准确率不高的问题,提出一种基于改进Adam优化算法的深度信念网络油井工况诊断方法.以二值化处理后的示功图作为深度信念网络输入,利用对比散度算法对网络进行无监督预训练,以获取较优的初始权值;在反向传播微调网络过程中,运用动量法预测梯度下降位置,更新梯度下降方向,并通过学习率自适应选择下降步长,避免梯度扩散降低模型训练效果.某采油平台油井上的仿真实验结果表明,基于改进Adam优化算法的深度信念网络对油井工况的识别准确率较高,能更好地满足油田生产实际需求.

Pure quantum gradient descent algorithm and full quantum variational eigensolver

Optimization problems are prevalent in various fields,and the gradient-based gradient descent algorithm is a widely adopted optimization method.However,in classical computing,computing the numerical gradient for a function with variables necessitates at least d+1 function evaluations,resulting in a computational complexity of O(d).As the number of variables increases,the classical gradient estimation methods require substantial resources,ultimately surpassing the capabilities of classical computers.Fortunately,leveraging the principles of superposition and entanglement in quantum mechanics,quantum computers can achieve genuine parallel computing,leading to exponential acceleration over classical algorithms in some cases.In this paper,we propose a novel quantum-based gradient calculation method that requires only a single oracle calculation to obtain the numerical gradient result for a multivariate function.The complexity of this algorithm is just O(1).Building upon this approach,we successfully implemented the quantum gradient descent algorithm and applied it to the variational quantum eigensolver(VQE),creating a pure quantum variational optimization algorithm.Compared with classical gradient-based optimization algorithm,this quantum optimization algorithm has remarkable complexity advantages,providing an efficient solution to optimization problems.The proposed quantum-based method shows promise in enhancing the performance of optimization algorithms,highlighting the potential of quantum computing in this field.

面向“云-边-端”算力系统的计算和传输联合优化方法

“云—边—端”资源协同优化是算力网络部署的关键难题之一.如何有效整合高性能云计算、低延时边缘计算和低成本用户设备等异构算力资源,对于算力网络建设具有重要意义.基于此,提出了一种面向“云—边—端”算力网络的计算和传输联合优化方案,旨在从应用服务模型、网络状态感知和资源联合优化3方面提供一套系统性解决方案.首先,根据通用应用服务的特性,改进了传统网络服务的链状表征模型,提出了广义图结构的通用服务模型.其次,为表征异构网络状态的动态规律,提出了用于建模计算和传输时变负载的双虚拟队列结构.再次,为降低在大规模算力网络中计算和传输资源联合优化的问题复杂度,提出了一种基于图概念的增广图模型,该模型能够将计算和传输联合优化问题转化为增广图的路由问题,简化了异构资源联合优化问题的形式化表征难度.为实际解决该问题,设计了基于波利亚重球法(Polyak heavy-ball method)的异构资源协同优化算法,并给出了算法复杂度和相关理论的性能分析.最后,通过数值仿真和原型系统实验,验证了算法理论性能的正确性,以及在服务效用和资源成本等方面对比同期3种相关解决方案的性能优势.

非凸-凹极小极大问题的双尺度交替梯度下降上升算法

针对一类非凸-(强)凹极小极大问题,基于双尺度梯度下降上升算法,用交替梯度更新来替代同步梯度更新,从而提出了一种新算法-双尺度交替梯度下降上升算法.通过数值实验结果表明,新算法在MNIST数据集上的分类准确率明显高于原算法,从而验证了新算法的有效性.

A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered.This problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated learning.We propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost functions.We show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of iterations.We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum.We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

基于SGDM优化IWOA-CNN的配电网工程造价控制研究

为了控制配电网工程项目的成本,需准确预测配电网工程造价,本文提出一种基于带动量因子的随机梯度下降(stochastic gradient descent with momentum factor, SGDM)优化的改进鲸鱼算法-卷积神经网络工程造价预测模型。首先,考虑回路数、杆塔数、导线、地形、地质、风速、覆冰、导线截面、混凝土杆、塔材、绝缘子(直线)、绝缘子(耐张)、基坑开方、基础钢材、底盘和水泥对配电网工程造价的影响,建立了非线性函数关系;采用SGDM优化器改进的卷积神经网络对函数进行逼近,并用贝叶斯方法优化卷积神经网络的超参数;利用改进的鲸鱼算法(improved whale optimization algorithm, IWOA)优化卷积神经网络,找出卷积神经网络的最优学习率。数值算例表明,新模型预测效果较好,并提出相应的控制策略。

PROJECTED GRADIENT DESCENT BASED ON SOFT THRESHOLDING IN MATRIX COMPLETION

Matrix completion is the extension of compressed sensing.In compressed sensing,we solve the underdetermined equations using sparsity prior of the unknown signals.However,in matrix completion,we solve the underdetermined equations based on sparsity prior in singular values set of the unknown matrix,which also calls low-rank prior of the unknown matrix.This paper firstly introduces basic concept of matrix completion,analyses the matrix suitably used in matrix completion,and shows that such matrix should satisfy two conditions:low rank and incoherence property.Then the paper provides three reconstruction algorithms commonly used in matrix completion:singular value thresholding algorithm,singular value projection,and atomic decomposition for minimum rank approximation,puts forward their shortcoming to know the rank of original matrix.The Projected Gradient Descent based on Soft Thresholding(STPGD),proposed in this paper predicts the rank of unknown matrix using soft thresholding,and iteratives based on projected gradient descent,thus it could estimate the rank of unknown matrix exactly with low computational complexity,this is verified by numerical experiments.We also analyze the convergence and computational complexity of the STPGD algorithm,point out this algorithm is guaranteed to converge,and analyse the number of iterations needed to reach reconstruction error.Compared the computational complexity of the STPGD algorithm to other algorithms,we draw the conclusion that the STPGD algorithm not only reduces the computational complexity,but also improves the precision of the reconstruction solution.

基于ILSTM-AMSGD神经网络的时间序列预测方法

针对标准长短期记忆(long short-term memory,LSTM)神经网络的结构参数众多、训练过程耗时长问题,提出一种基于自适应动量随机梯度下降(adaptive momentum stochastic gradient descent,AMSGD)算法的改进型长短期记忆神经网络(ILSTM-AMSGD神经网络),并将其用于时间序列预测中。首先,通过简化结构方程中的递归项权值,减少网络中所需训练的参数。其次,设计一种AMSGD算法对神经网络结构参数进行学习。最后,通过2个基准数据集和1个实际数据集对ILSTM-AMSGD神经网络模型在时间序列预测中的准确性和运行效率进行实验验证。结果表明,递归项权值简化方法可以提高模型的泛化能力,同时AMSGD算法加快了模型的收敛速度。与其他模型相比,ILSTM-AMSGD神经网络模型实现了对时间序列更加高效、准确的预测。

基于MSADE-IT2FNN模型的软测量建模方法及应用

针对复杂工业过程某些关键参数无法有效、实时在线检测的问题,提出一种基于多策略、自适应差分进化算法(MSADE)优化的区间二型模糊神经网络(IT2FNN)软测量建模方法。首先,为了解决差分进化算法采用单一策略、固定缩放因子和交叉概率导致后期搜索能力不足的问题,提出一种多策略、自适应的差分进化算法(MSADE),该算法利用IT2FNN模型的均方根误差(RMSE)作为适应度函数,通过搜索不同规则数下的RMSE值,从而确定IT2FNN的结构(规则数)和初始参数;然后,IT2FNN模型的参数利用梯度下降法进行学习。最后,将所提模型应用到Mackey-Glass混沌时间序列的预测和酿酒过程淀粉利用率的软测量建模问题中,仿真结果验证了提出方法的有效性和优越性。

多径异步LSC-DS-CDMA信号伪码估计

针对多径环境下异步长短码直扩码分多址信号(long and short code direct sequence code division multiple access,LSC-DS-CDMA)伪码估计难的问题,提出一种基于张量分解和联合估计的伪码估计方法,采用重叠窗对接收信号进行分段并构建张量模型。为改善传统线性步长搜索算法结合梯度下降的方法分解因子矩阵收敛较慢的问题,提出改进的线性步长搜索算法,结合使用动量梯度下降法对各子张量进行Tucker分解得到各因子矩阵,所需的迭代次数大大减少;利用接收增益矩阵和移位相乘解决复合码的排序模糊和幅度模糊问题;利用最大似然准则联合估计复合码和多径信道后,使用梅西算法和相关运算估计每个用户的长码和短码。仿真结果表明,该方法能够有效估计多径异步LSC-DS-CDMA信号的伪码。

带有随机改进Barzilai-Borwein步长的小批量稀疏随机方差缩减梯度法

为了更好地应对当今时代的大规模高维稀疏数据集,融合BB方法、小批量算法与随机方差缩减梯度法(SVRG)优势,提出一种带有随机改进Barzilai-Borwein步长的小批量稀疏随机方差缩减梯度法(MSSVRG-R2BB)。首先,在SVRG外循环中全梯度计算的基础上加入L_1范数次梯度设计出一种稀疏近似梯度用于内循环,得到一种稀疏的SVRG算法(SSVRG)。在此基础上,在小批量的稀疏随机方差缩减梯度法中使用随机选取的改进BB方法自动计算、更新步长,解决了小批量算法的步长选取问题,拓展得到MSSVRG-R2BB算法。数值实验表明,在求解大规模高维稀疏数据的线性支持向量机(SVM)问题时,MSSVRG-R2BB算法不仅可以减小运算成本、更快达到收敛上界,同时能达到与其他先进的小批量算法相同的优化水平,并且对于不同的初始参数选取表现稳定且良好。

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.

基于动作时限曲线拟合思想的含DG型配电网后备保护新方法

分布式电源(distributed generation,DG)的接入可能会改变电力系统的短路电流特性,从而影响到反时限过流保护的选择性和配合关系。为此,提出一种基于动作时限曲线拟合思想的含DG型配电网后备保护新方法。从配电网主保护和后备保护的动作时间差入手,建立曲线拟合模型,并运用梯度下降法对反时限过流保护的动作时限曲线进行优化。通过在优化整定计算中考虑保护的选择性以及DG接入所带来的影响,使优化参数适用于配电网中各分段位置。然后,在配电网中设置两相、三相短路故障,并根据故障位置、类型进行整定参数再调整,最终得到参数的全局最优解。基于PSCAD/EMTDC仿真软件进行仿真分析,验证了所提方法的有效性和实用性。

分布式训练系统及其优化算法综述

人工智能利用各种优化技术从海量训练样本中学习关键特征或知识以提高解的质量,这对训练方法提出了更高要求.然而,传统单机训练无法满足存储与计算性能等方面的需求.因此,利用多个计算节点协同的分布式训练系统成为热点研究方向之一.本文首先阐述了单机训练面临的主要挑战.其次,分析了分布式训练系统亟需解决的三个关键问题.基于上述问题归纳了分布式训练系统的通用框架与四个核心组件.围绕各个组件涉及的技术,梳理了代表性研究成果.在此基础之上,总结了基于并行随机梯度下降算法的中心化与去中心化架构研究分支,并对各研究分支优化算法与应用进行综述.最后,提出了未来可能的研究方向.

基于知识模糊迁徙的城市污水处理膜污染决策

针对城市污水处理膜污染难以精准决策的问题,提出一种基于知识模糊迁徙的膜污染决策方法。首先,结合城市污水处理运行过程数据和运行经验,利用模糊规则的形式实现膜污染决策知识的表达;其次,提出一种知识重构机制(knowledge reconstruction mechanism,KRM),动态平衡源域与目标域之间的准确性和多样性,并采用知识迁徙的方法完成决策知识重构;最后,建立一种基于数据和知识驱动的区间二型模糊神经网络(data-knowledge-driven interval type-2 fuzzy neural network,DK-IT2FNN)的决策模型,利用模糊规则设计模型参数,采用迁徙梯度下降算法动态调整网络权值,提高决策精度。实验结果表明,该模型能够实现膜污染的精准决策。