The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved ...为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved sparrow search algorithm optimized deep belief network,简称FISSA-DBN)的风电机组状态监测新方法。首先,对SCADA数据进行预处理分析,并利用专家系统和皮尔逊相关系数分析,相关分析选取输入参数和输出参数;其次,利用预处理数据集建立基于FISSA-DBN的风电机组运行状态监测新模型,根据模型预测值和实际输出值之间的重构值误差,以及指数加权移动平均阈值(exponentially weighted moving average,简称EWMA)判断是否有异常;最后,以华东某风电场实际数据为例进行实例验证。结果表明,所提出方法的预警时间比实际记录时间最早可提前4 d多。同时,将所提出方法与其他方法进行对比,结果表明该方法预警时间提前,模型预测误差更小。展开更多
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
文摘为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved sparrow search algorithm optimized deep belief network,简称FISSA-DBN)的风电机组状态监测新方法。首先,对SCADA数据进行预处理分析,并利用专家系统和皮尔逊相关系数分析,相关分析选取输入参数和输出参数;其次,利用预处理数据集建立基于FISSA-DBN的风电机组运行状态监测新模型,根据模型预测值和实际输出值之间的重构值误差,以及指数加权移动平均阈值(exponentially weighted moving average,简称EWMA)判断是否有异常;最后,以华东某风电场实际数据为例进行实例验证。结果表明,所提出方法的预警时间比实际记录时间最早可提前4 d多。同时,将所提出方法与其他方法进行对比,结果表明该方法预警时间提前,模型预测误差更小。
