• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers and Electronics in Agriculture >Observer Kalman filter identification and multiple-model adaptive estimation technique for classifying animal behaviour using wireless sensor networks.
【24h】

Observer Kalman filter identification and multiple-model adaptive estimation technique for classifying animal behaviour using wireless sensor networks.

机译：使用无线传感器网络对动物行为进行分类的观察者卡尔曼滤波器识别和多模型自适应估计技术。

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The identification of a mathematical model capable of describing the behaviour of animals given input such as feed has great potential for behavioural control purposes. Such models will allow to make predictions which are fundamental to any closed loop control such as control of the feeding. This paper investigates the problem of mathematically modelling animal behaviour. An observer Kalman filter identification method was successfully applied to input-output data and two models representing the hypotheses that animals are actively feeding and the hypotheses that animals are inactive were identified. The input and output of each of the identified models were feed dry matter offer and the pitch angle of the neck, respectively. The pitch angle of the neck of the animal was successfully measured and aggregated by a ZigBee-based wireless sensor network. Two fourth-order models describing the dynamics of an animal in the active and inactive behaviour modes showed good performance in terms of prediction error, cross-correlation between the residual and the output as well as cross-correlation between the residual and the input with 99% confidence interval. A multiple-model adaptive estimation approach was applied to determine the likelihood of each of the two models being the correct model for a specific input of dry matter feed. The average classification success rate was 87.2% for the whole experiment.
机译：能够描述在给定输入（例如饲料）的情况下能够描述动物行为的数学模型的识别对于行为控制具有很大的潜力。这样的模型将允许做出对于任何闭环控制（例如进料控制）至关重要的预测。本文研究了对动物行为进行数学建模的问题。一种观察者卡尔曼滤波器的识别方法已成功地应用于输入-输出数据，并识别了两个表示动物正在主动喂养的假设和动物不活跃的假设的模型。每个确定模型的输入和输出分别是饲料干物质供给和颈部的俯仰角。通过基于ZigBee的无线传感器网络成功测量并汇总了动物脖子的俯仰角。描述动物在活动和非活动行为模式下的动力学的四个四阶模型在预测误差，残差和输出之间的互相关以及残差和输入与99之间的互相关方面表现出良好的性能％置信区间。应用多模型自适应估计方法来确定两个模型中的每一个对于干物质饲料的特定输入而言都是正确模型的可能性。整个实验的平均分类成功率为87.2％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号