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New multi-layer data correlation algorithm for multi-passive-sensor location system 认领 引用 被引量：1
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作者 Zhou Li Li Lingyun He You 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第4期667-672,共6页
Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough corr... Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective. 展开更多
关键词 multi-passive-sensor data correlation multi-layer correlation algorithm location system correlation cost
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Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models 认领 引用 被引量：5
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作者 SHUI Kuan HOU Ke-peng +2 位作者 HOU Wen-wen SUN Jun-long SUN Hua-fen 《Journal of Mountain Science》 SCIE CSCD 2023年第10期2852-2868,共17页
The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o... The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments. 展开更多
关键词 Multi-layer regression algorithm fusion Stacking gensemblelearning Sparrow search algorithm Slope safety factor Data prediction
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data 认领 引用 被引量：6
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作者 Lun Gao Ran-Hong Xie +2 位作者 Li-Zhi Xiao Shuai Wang Chen-Yu Xu 《Petroleum Science》 SCIE CAS CSCD 2022年第2期570-580,共11页
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca... In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%. 展开更多
关键词 Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning DBSCAN algorithm Multi-layer perceptron
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Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network 认领 引用 被引量：17
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作者 Bhatawdekar Ramesh Murlidhar Hoang Nguyen +4 位作者 Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第6期1413-1427,共15页
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t... In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R2),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R2 of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. 展开更多
关键词 Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)
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基于多层VSA-Morphin算法的局部路径规划 认领 引用 被引量：2
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作者 邝先验 欧阳鹏 +1 位作者 周亚龙 罗会超 《电子测量与仪器学报》 CSCD 北大核心 2020年第2期123-129,共7页
多层Morphin算法扩展了对未知环境的预测范围,克服了传统Morphin算法搜索轨迹不灵活的缺点,但每个搜索节点生成的搜索弧数目固定,搜索和评估所花费的时间随着搜索层数的增多呈指数阶增加。针对该问题,提出了一种可变搜索弧Morphin算法(v... 多层Morphin算法扩展了对未知环境的预测范围,克服了传统Morphin算法搜索轨迹不灵活的缺点,但每个搜索节点生成的搜索弧数目固定,搜索和评估所花费的时间随着搜索层数的增多呈指数阶增加。针对该问题,提出了一种可变搜索弧Morphin算法(variable search arc of Morphin,VSA-Morphin)。调整每层搜索节点生成的搜索弧数目,使之不再固定,而是随着层数增加而减少,从而缩短搜索和评估时间。利用MATLAB仿真测试表明,多层VSA-Morphin算法与多层Morphin算法所规划的路径基本一致,但运行时间却相对更少,从而验证了多层VSA-Morphin算法的有效性和正确性。 展开更多
关键词 多层Morphin算法 多层VSA-Morphin算法 局部路径规划
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Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Populationphysic-based Algorithm 认领 引用 被引量：7
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作者 Sajjad Afrakhteh Mohammad-Reza Mosavi +1 位作者 Mohammad Khishe Ahmad Ayatollahi 《International Journal of Automation and computing》 CSCD 2020年第1期108-122,共15页
A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their... A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others. 展开更多
关键词 Brain-computer interface(BCI) classification electroencephalography(EEG) gravitational search algorithm(GSA) multi-layer perceptron neural network(MLP-NN) particle swarm optimization
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基于改进蚁群算法与Morphin算法的机器人路径规划方法 认领 引用 被引量：14
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作者 万晓凤 胡伟 +1 位作者 郑博嘉 方武义 《科技导报》 CAS CSCD 北大核心 2015年第3期84-89,共6页
针对动态复杂环境下的机器人路径规划问题,建立栅格地图模型,研究一种改进蚁群算法与Morphin算法相结合的动态路径规划方法。改进蚁群算法引入拐点参数评价路径优劣,并对路径进行拐角处理以及变更拐角处信息素更新机制,使规划的全局路... 针对动态复杂环境下的机器人路径规划问题,建立栅格地图模型,研究一种改进蚁群算法与Morphin算法相结合的动态路径规划方法。改进蚁群算法引入拐点参数评价路径优劣,并对路径进行拐角处理以及变更拐角处信息素更新机制,使规划的全局路径更加平滑;Morphin算法则在机器人行走时,根据全局路径的局部环境实时规划局部路径,使机器人有效地躲避障碍物。仿真试验结果表明,该方法结合全局规划与局部规划的特点,能够使机器人沿着一条短而平滑的最优路径快速、安全地到达目标点。 展开更多
关键词 动态路径规划 改进蚁群算法 Morphin算法 拐角处理
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改进QPSO和Morphin算法下移动机器人混合路径规划 认领 引用 被引量：19
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作者 伍永健 陈跃东 陈孟元 《电子测量与仪器学报》 CSCD 北大核心 2017年第2期295-301,共7页
为了提高机器人在复杂环境下路径规划的能力,提出了一种基于改进量子粒子群优化算法(QPSO)和Morphin算法的混合路径规划方法。利用栅格地图建立环境模型并确定起始点和目标点,通过引入自适应局部搜索策略和交叉操作对QPSO进行改进规划... 为了提高机器人在复杂环境下路径规划的能力,提出了一种基于改进量子粒子群优化算法(QPSO)和Morphin算法的混合路径规划方法。利用栅格地图建立环境模型并确定起始点和目标点,通过引入自适应局部搜索策略和交叉操作对QPSO进行改进规划出一条最优的全局路径,机器人根据全局路径行走,当发现未知静态或动态障碍物立即调用Morphin算法进行局部路径规划,避开障碍物后回到原全局路径上继续行走至目标点。该混合路径规划方法的有效性和可行性通过Matlab仿真和实际应用得到很好地验证。 展开更多
关键词 复杂环境 移动机器人 障碍物 改进QPSO Morphin算法 混合路径规划
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基于改进遗传算法与Morphin算法的机器人路径规划方法(英文) 认领 引用 被引量：3
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作者 钟佩思 李潭潭 +2 位作者 刘梅 陈修龙 张幸兰 《机床与液压》 北大核心 2019年第24期33-38,共6页
为了实现动态复杂环境下机器人路径的优化,在建立栅格地图模型的基础上,针对传统遗传算法的不足,一是通过改进适应度函数使得到的路径更加平滑,二是将改进后的遗传算法与Morphin算法结合起来,使得机器人能够实时有效的躲避障碍物。仿真... 为了实现动态复杂环境下机器人路径的优化,在建立栅格地图模型的基础上,针对传统遗传算法的不足,一是通过改进适应度函数使得到的路径更加平滑,二是将改进后的遗传算法与Morphin算法结合起来,使得机器人能够实时有效的躲避障碍物。仿真实验结果表明:通过结合改进遗传算法和Morphin算法的特点,能够使机器人沿着一条短而平滑的最优路径快速、安全地到达目标点。 展开更多
关键词 改进遗传算法 Morphin算法 动态环境 路径规划
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危险天气下基于多重Morphin算法的终端区三维实时改航方法 认领 引用 被引量：6
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作者 张兆宁 魏中慧 《南京航空航天大学学报（自然科学版）》 CAS CSCD 北大核心 2015年第4期467-473,共7页
基于多重Morphin算法,建立了终端区三维实时改航方法。该方法首先根据航空器当前飞行状态按不同的转弯角和爬升/下滑角生成一组弧线,随后在每条弧线的末端按同样方式反复运行,形成若干条由弧线组成的路径,最后对所有路径进行综合评估,... 基于多重Morphin算法,建立了终端区三维实时改航方法。该方法首先根据航空器当前飞行状态按不同的转弯角和爬升/下滑角生成一组弧线,随后在每条弧线的末端按同样方式反复运行,形成若干条由弧线组成的路径,最后对所有路径进行综合评估,找到当前时刻的改航路径。算例分析表明,该方法提供的改航路径可以保证航空器运行的安全与高效,在危险天气出现时更加充分地利用终端区空域资源,同时计算时间短、可行性高。 展开更多
关键词 实时改航 三维改航 危险天气 终端区 Morphin算法
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改进鸽群和Morphin算法的混合路径规划算法研究 认领 引用 被引量：1
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作者 支奕琛 谷玉海 +1 位作者 徐小力 龙伊娜 《机械设计与制造》 北大核心 2024年第6期53-57,63,共5页
针对目前无人车在复杂环境例如同时存在静态和动态障碍物的环境下路径规划能力较弱的情况,提出了一种引入自适应权重系数的鸽群算法和Morphine算法的混合路径规划算法。(1)在栅格地图中确定起点和终点同时建立环境模型;(2)在鸽群算中加... 针对目前无人车在复杂环境例如同时存在静态和动态障碍物的环境下路径规划能力较弱的情况,提出了一种引入自适应权重系数的鸽群算法和Morphine算法的混合路径规划算法。(1)在栅格地图中确定起点和终点同时建立环境模型;(2)在鸽群算中加入自适应权重系数进行改进从而规划出一条全局最优路径,无人车按照全局路径行驶,当无人车的传感器探测到未知的静态或动态障碍物情况下,将会立刻运用Morphine算法进行相应的路线设计,从而实现对障碍物的躲避,无人车躲避障碍物后回到原来的路径上继续行驶至目标点。通过仿真实验和在无人车上的实际应用验证了该混合路径规划算法的有效性和可行性。 展开更多
关键词 无人车 障碍物 改进鸽群算法 Morphine算法
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融合改进A*算法和Morphin算法的移动机器人动态路径规划 认领 引用 被引量：18
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作者 成怡 肖宏图 《智能系统学报》 CSCD 北大核心 2020年第3期546-552,共7页
在动态未知环境下对机器人进行路径规划,传统A*算法可能出现碰撞或者路径规划失败问题。为了满足移动机器人全局路径规划最优和实时避障的需求,提出一种改进A*算法与Morphin搜索树算法相结合的动态路径规划方法。首先通过改进A*算法减... 在动态未知环境下对机器人进行路径规划,传统A*算法可能出现碰撞或者路径规划失败问题。为了满足移动机器人全局路径规划最优和实时避障的需求,提出一种改进A*算法与Morphin搜索树算法相结合的动态路径规划方法。首先通过改进A*算法减少路径规划过程中关键节点的选取,在规划出一条全局较优路径的同时对路径平滑处理。然后基于移动机器人传感器采集的局部信息,利用Morphin搜索树算法对全局路径进行动态的局部规划,确保更好的全局路径的基础上,实时避开障碍物行驶到目标点。MATLAB仿真实验结果表明,提出的动态路径规划方法在时间和路径上得到提升,在优化全局路径规划的基础上修正局部路径,实现动态避障提高机器人达到目标点的效率。 展开更多
关键词 移动机器人 A*算法 改进A*算法 Morphin搜索树算法 全局路径规划 局部路径规划 动态路径规划 实时避障
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激光数据聚类和Morphin算法下的机器人避障研究 认领 引用 被引量：2
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作者 刁海婷 李劲松 陈孟元 《佳木斯大学学报（自然科学版）》 CAS 2018年第6期886-890,共5页
为了解决移动机器人在未知环境下的避障问题,提出了一种从障碍物检测、预测到避撞的避障方法。设定圆形窗口作为机器人有效扫描区域,利用激光传感器采集到的数据结合聚类、匹配和分类算法确定障碍物类型和动态障碍物运动信息,绘制窗口... 为了解决移动机器人在未知环境下的避障问题,提出了一种从障碍物检测、预测到避撞的避障方法。设定圆形窗口作为机器人有效扫描区域,利用激光传感器采集到的数据结合聚类、匹配和分类算法确定障碍物类型和动态障碍物运动信息,绘制窗口内的动态局部地图来预测动态障碍物与机器人的碰撞关系,结合Morphin算法实现有效的避障。仿真实验表明,在该算法下移动机器人能够有效地检测出障碍物,进行碰撞预测,并做出合理地避障措施。 展开更多
关键词 激光传感器 检测 预测 避障 聚类 Morphin算法
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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron 认领 引用 被引量：3
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作者 Sajad Ahmad Rather P.Shanthi Bala 《International Journal of Intelligent Computing and Cybernetics》 EI 2020年第2期129-165,共37页
Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcom... Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup. 展开更多
关键词 Neural network Feedforward neural network(FNN) Gravitational search algorithm(GSA) Particle swarm optimization(PSO) Hybridization CPSOGSA Multi-layer perceptron(MLP)
Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates 认领 引用 被引量：3
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作者 Neil Vaz Jaeyoo Choi +3 位作者 Yohan Cha Jihoon Kong Yooseong Park Hyunchul Ju 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第6期28-41,I0003,共14页
Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectivenes... Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(Vcell)also PEMFC fabrication and production(Cstack)costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(LPt),weight ratio of ionomer to carbon(wtI/C),weight ratio of Pt to carbon(wtPt/c),and porosity of cathode CL(εcCL),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R2.MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance. 展开更多
关键词 Polymer electrolyte membrane fuel cell Surrogate modeling Multi-layer perceptron(MLP) Response surface analysis(RSA) Non-dominated sorting genetic algorithmⅡ(NSGAⅡ)
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移动机器人动态路径规划方法的研究与实现 认领 引用 被引量：8
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作者 史进 董瑶 +2 位作者 白振东 崔泽晨 董永峰 《计算机应用》 CSCD 北大核心 2017年第11期3119-3123,共5页
针对在未知动态障碍物存在且目标点移动的环境下,采用人工势场法规划路径时斥力影响半径往往大于障碍物的半径从而导致动态障碍物与机器人发生碰撞的问题,提出非完全等待策略与Morphine算法相结合的改进人工势场法动态路径规划策略。当... 针对在未知动态障碍物存在且目标点移动的环境下,采用人工势场法规划路径时斥力影响半径往往大于障碍物的半径从而导致动态障碍物与机器人发生碰撞的问题,提出非完全等待策略与Morphine算法相结合的改进人工势场法动态路径规划策略。当动态障碍物与机器人发生侧面碰撞时采用非完全等待策略;当动态障碍物与机器人发生迎面碰撞时采用Morphine算法局部规划路径;同时引入滚动窗口理论提高躲避动态障碍物的精确度。通过仿真实验,与传统人工势场作对比,提出的改进算法在发生侧面碰撞时要缩短12步,在发生迎面碰撞时要缩短6步,由此可得提出改进算法在路径平滑性和规划步数方面效果更优。 展开更多
关键词 路径规划 人工势场 Morphine算法 非完全等待策略 滚动窗口
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Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar 认领 引用 被引量：5
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作者 ZHOU Hui-lin TIAN Mao CHEN Xiao-li 《Wuhan University Journal of Natural Sciences》 EI CAS 2005年第6期1009-1012,共4页
Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper ... Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented. 展开更多
关键词 ground penetrating radar nonstationary signal dyadic wavelet transform feed-forward multi-layer perceptron back propagation algorithm
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Effective prediction of DEA model by neural network 认领 引用
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作者 孙佰清 董靖巍 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第5期683-686,共4页
In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow conv... In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid. 展开更多
关键词 multi-layer neural network single parameter dynamic searching algorithm BP algorithm DEA forecasting
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一种改进的未知动态环境下机器人混合路径规划方法 认领 引用 被引量：6
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作者 曹清云 倪建军 +1 位作者 王康 吴榴迎 《计算机与现代化》 2016年第4期54-58,共5页
动态未知环境下的机器人路径规划是机器人导航领域的重要课题之一,采用传统的方法求解并不理想。针对这个问题,提出一种改进的机器人混合路径规划方法。首先利用改进的文化基因算法规划出较优的全局路径,指引机器人沿着全局路径行走,然... 动态未知环境下的机器人路径规划是机器人导航领域的重要课题之一,采用传统的方法求解并不理想。针对这个问题,提出一种改进的机器人混合路径规划方法。首先利用改进的文化基因算法规划出较优的全局路径,指引机器人沿着全局路径行走,然后根据传感器探测到的局部环境信息,利用Morphin算法进行局部路径实时规划,使机器人有效地躲避动态障碍物。仿真实验表明,该算法在未知动态路径规划中具有良好的效果。 展开更多
关键词 动态路径规划 文化基因算法 Morphin算法 混合 未知环境
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