This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the ...This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model(DNM)is used to learn the behavior of the system, then, an inverse neural model(INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation(BP) algorithm. In this work, the sliding mode-backpropagation(SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.展开更多
The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner dia...The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.展开更多
为了降低在网络教学中,由于学生自主学习行为的多样性和技术平台存在的差异性,导致传统评价方法难以准确给出评价反馈的问题,引入LMBP(Levenberg-Marquardt Back Propagation)算法,构建了一个能够利用权重化的评价指标对学生的学习表现...为了降低在网络教学中,由于学生自主学习行为的多样性和技术平台存在的差异性,导致传统评价方法难以准确给出评价反馈的问题,引入LMBP(Levenberg-Marquardt Back Propagation)算法,构建了一个能够利用权重化的评价指标对学生的学习表现进行量化分析的自动评价模型。确定网络教学在线学习的评价指标权重,筛选出关键评价指标,并合理分配权重值,降低数据的无序性。基于LMBP算法构建自动评价模型,通过模型的运算,自动计算出每个学生的在线学习评价分数,降低评价的滞后性,实现客观、准确的评价。实验结果显示,模型计算得到的各项指标权重值在0.96以上,拟合度高于0.98,评价分数高于97分,可以实现网络教学的有效评价。展开更多
文摘This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model(DNM)is used to learn the behavior of the system, then, an inverse neural model(INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation(BP) algorithm. In this work, the sliding mode-backpropagation(SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.
基金supported by the National Natural Science Foundation of China(Grant No.52271277)the Natural Science Foundation of Jiangsu Province(Grant.No.BK20211343)+1 种基金the State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University)(Grant.No.GKZD010081)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant.No.SJCX22_1906).
文摘The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.
文摘为了降低在网络教学中,由于学生自主学习行为的多样性和技术平台存在的差异性,导致传统评价方法难以准确给出评价反馈的问题,引入LMBP(Levenberg-Marquardt Back Propagation)算法,构建了一个能够利用权重化的评价指标对学生的学习表现进行量化分析的自动评价模型。确定网络教学在线学习的评价指标权重,筛选出关键评价指标,并合理分配权重值,降低数据的无序性。基于LMBP算法构建自动评价模型,通过模型的运算,自动计算出每个学生的在线学习评价分数,降低评价的滞后性,实现客观、准确的评价。实验结果显示,模型计算得到的各项指标权重值在0.96以上,拟合度高于0.98,评价分数高于97分,可以实现网络教学的有效评价。
