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基于EtherCAT和遗传-BP神经网络的等温锻造电液伺服系统优化研究 被引量:6
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作者 李欣 王晓燕 《机电工程》 CAS 北大核心 2019年第5期534-538,共5页
针对等温锻造电液伺服系统有限滑块行程内锻造载荷和滑块速度控制精度低、横梁调平状态不稳定等问题,提出了一种基于EtherCAT和遗传-BP神经网络的等温锻造工艺参数优化控制系统。利用LabVIEW设计了主站监控界面,配置了各模块间通信协议,... 针对等温锻造电液伺服系统有限滑块行程内锻造载荷和滑块速度控制精度低、横梁调平状态不稳定等问题,提出了一种基于EtherCAT和遗传-BP神经网络的等温锻造工艺参数优化控制系统。利用LabVIEW设计了主站监控界面,配置了各模块间通信协议,用ActiveX设计了遗传-BP神经网络与LabVIEW间通信接口,通过UDP/IP在LabVIEW和EtherCAT从站间交互实时监控数据,利用仿真模拟了非线性模型的优化精度,并实时监控了柱坯等温锻造过程的横梁状态。研究结果表明:经遗传-BP神经网络算法优化,载荷和滑块速度的整体控制误差低于2.5%,EtherCAT从站反馈的横梁调平状态稳定,有效改善了等温锻造电液伺服系统存在的问题,提高了材料利用率和锻件成型质量。 展开更多
关键词 等温锻造电液伺服系统 ETHERCAT LabVIEW 遗传-bp神经网络
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高速铣削工件表面粗糙度遗传-BP神经网络建模 被引量:2
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作者 黄希宇 祁翔 《智能计算机与应用》 2021年第2期183-186,共4页
遗传算法作为一种高效,并行的全局搜索优化方法,非常适合用于BP神经网络学习率的优化。文中通过基于遗传算法和BP神经网络提出了遗传-BP神经网络。以实验1、实验2、实验5、实验6、实验9、实验11、实验13和实验15下的高速铣削试验数据构... 遗传算法作为一种高效,并行的全局搜索优化方法,非常适合用于BP神经网络学习率的优化。文中通过基于遗传算法和BP神经网络提出了遗传-BP神经网络。以实验1、实验2、实验5、实验6、实验9、实验11、实验13和实验15下的高速铣削试验数据构建用于高速铣削工件表面粗糙度建模的训练样本对,并用回归的高速铣削工件表面粗糙度预测模型对实验3和实验7状态中的高速铣削工件表面粗糙度进行预测。通过比较表面粗糙度预测结果和实际结果,发现遗传-BP神经网络在高速铣削工件表面粗糙度进行建模方面是一种十分有效的方法。 展开更多
关键词 高速铣削 表面粗糙度 预测 遗传-bp人工神经网络
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基于遗传算法-BP神经网络的煤层注水效果分析 被引量:7
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作者 刘锦伟 谢雄刚 方井 《工矿自动化》 北大核心 2016年第1期48-51,共4页
为了提高BP神经网络预测煤层注水效果的精度,采用遗传算法优化BP神经网络的权值和阈值,建立了遗传算法-BP神经网络模型,并采用该模型对煤层注水湿润半径进行模拟预测。Matlab模拟结果表明,遗传算法-BP神经网络模型的预测结果比BP神经网... 为了提高BP神经网络预测煤层注水效果的精度,采用遗传算法优化BP神经网络的权值和阈值,建立了遗传算法-BP神经网络模型,并采用该模型对煤层注水湿润半径进行模拟预测。Matlab模拟结果表明,遗传算法-BP神经网络模型的预测结果比BP神经网络模型更准确,平均相对误差降低了40.29%,训练步数减少了1 665步,收敛速度快,稳定性好。 展开更多
关键词 煤层注水 BP神经网络 遗传算法-bp神经网络模型 湿润半径
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应用GA-BP神经网络优化平摆复合振动筛的振动参数 被引量:6
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作者 沈国浪 童昕 李占福 《华侨大学学报(自然科学版)》 CAS 北大核心 2018年第4期509-513,共5页
针对目前筛分理论的研究仅局限于单因素考虑的问题,提出应用遗传算法(GA)优化的BP神经网络对数据空间进行全局寻优,且考虑所有因素对筛分结果的综合影响.首先,通过离散单元法的筛分仿真试验,获取实际筛分过程中难以获取的数据.然后,利... 针对目前筛分理论的研究仅局限于单因素考虑的问题,提出应用遗传算法(GA)优化的BP神经网络对数据空间进行全局寻优,且考虑所有因素对筛分结果的综合影响.首先,通过离散单元法的筛分仿真试验,获取实际筛分过程中难以获取的数据.然后,利用GA优化的BP神经网络对平摆复合振动筛的振动参数进行优化,选择5-9-1的BP神经网络结构类型,得到优化后的振动参数组合,即振幅为2 mm,振动频率为26Hz,振动方向角为46°,摆动频率为21Hz,摆角为1°.对优化后的结果进行一次模拟仿真验证,结果表明:验证结果与测试结果相吻合. 展开更多
关键词 振动筛 振动参数 离散单元法 筛分效率 遗传算法-bp神经网络
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考虑功率分布特性的微网风电功率预测模型 被引量:10
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作者 任德江 吴杰康 毛骁 《智慧电力》 北大核心 2018年第12期56-62,共7页
针对微网中风电功率预测模型输入数据分布不均匀特性导致其预测精度低的问题,在不改变原始数据的情况下,提出一种混合归一化方法改善输入数据的分布特性。目前风电预测模型主要使用的是单一的BP神经网络模型,考虑到该模型有容易陷入局... 针对微网中风电功率预测模型输入数据分布不均匀特性导致其预测精度低的问题,在不改变原始数据的情况下,提出一种混合归一化方法改善输入数据的分布特性。目前风电预测模型主要使用的是单一的BP神经网络模型,考虑到该模型有容易陷入局部最优、预测精度低等缺点,提出混沌遗传-BP神经网络风电功率预测模型,采用混沌遗传算法优化神经网络权值与阈值,因而该模型在全局区域内能保证较好的预测精度且不会陷入局部最小。算例结果表明:该混合归一化方法能够有效地改善输入数据的分布特性,且所提预测模型有更优的预测性能。 展开更多
关键词 风电功率预测模型 线性归一化 分布特性 混沌遗传-bp神经网络
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Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm 被引量:2
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作者 罗剑飞 林炜铁 +1 位作者 蔡小龙 李敬源 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第5期950-957,共8页
Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Exper... Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g 2 NO-2-N·(g MLSS)-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components (g·L-1 ): NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium. 展开更多
关键词 BP neural network genetic algorithm OPTIMIZATION nitrite oxidization rate nitrite-oxidizing bacteria
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Neural network fault diagnosis method optimization with rough set and genetic algorithms
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作者 孙红岩 《Journal of Chongqing University》 CAS 2006年第2期94-97,共4页
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th... Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly. 展开更多
关键词 rough sets genetic algorithm BP algorithms artificial neural network encoding rule
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FORECASTING TIME SERIES WITH GENETIC PROGRAMMING BASED ON LEAST SQUARE METHOD 被引量:3
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作者 YANG Fengmei LI Meng +1 位作者 HUANG Anqiang LI Jian 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2014年第1期117-129,共13页
Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory p... Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method: Genetic programming based on least square method (GP-LSM). Inheriting the advantages of genetic algorithm (GA), without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method (LSM). In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average (SARIMA) and back propagation artificial neural networks (BP-ANN). The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others. 展开更多
关键词 FORECAST genetic programming least square method time series.
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Multi-objective hydraulic optimization and analysis in a minipump 被引量:1
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作者 Bin Duan Minqing Luo +1 位作者 Chao Yuan Xiaobing Luo 《Science Bulletin》 SCIE EI CAS CSCD 2015年第17期1517-1526,共10页
Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characte... Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characterize and optimize a particular mechanical minipump. The optimization work was conducted to cope with the conflict between pressure head and hydraulic efficiency by an improved back-propagation neural network (BPNN) with the non-dominated sorting genetic algorithm-II (NSGA-II). The improved BPNN was utilized to predicate hydraulic performance and, moreover, was modified to improve the prediction accuracy. The NSGA-II was processed for minipump multi-objective optimization which is dominated by four impeller dimensions. During hydraulic optimization, the processing feasibility was also taken into consideration. Experiments were conducted to validate the above optimization methods. It was proved that the optimized minipump was improved by about 24 % in pressure head and 4.75 % in hydraulic efficiency compared to the original designed prototype. Meanwhile, the sensitivity test was used to analyze the influence of the four impeller dimensions. It was found that the blade outlet angle β2 and the impeller inlet diameter Do significantly influence the pressure head H and the hydraulic efficiency η, respec- tively. Detailed internal flow fields showed that the optimum model can relieve the impeller wake and improve both the pressure distribution and flow orientation. 展开更多
关键词 Minipump OPTIMIZATION Back-propagation neural network Non-dominated sorting genetic algorithm-II
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