Serveral methods for the pararrel acquisition of a PN sequence in a baseband direct sequence spread spectrum system are investigated. Four different kinds of schemes are considered: the optimal estimation scheme, the ...Serveral methods for the pararrel acquisition of a PN sequence in a baseband direct sequence spread spectrum system are investigated. Four different kinds of schemes are considered: the optimal estimation scheme, the locally optimal estimation scheme, the optimal testing searches and the locally optimal testing scheme. In the four kinds of parallel acquisition schemes, the expressions for the probability of error are given and compared with the actual error probabilities obtained via Monte Carlo simulation. We also outline a technique that can be suboptimal because of a large amount of hardware and computation when using the parallel acquisition schemes.展开更多
为了保证数字温度传感器对温度的准确读取,需要对传感器温度误差展开有效控制。为了提高数字温度传感器温度误差控制效果,提出基于混沌误差反向传播(Error Back Propagation Training,BP)神经网络算法的数字温度传感器温度误差模糊控制...为了保证数字温度传感器对温度的准确读取,需要对传感器温度误差展开有效控制。为了提高数字温度传感器温度误差控制效果,提出基于混沌误差反向传播(Error Back Propagation Training,BP)神经网络算法的数字温度传感器温度误差模糊控制方法。首先利用经验模态分解法对收集到的温度测量数据展开去噪处理,计算不同温度区间内的温度测量超差概率,进而实现误差特征阈值的提取;建立三层神经网络,通过对温度误差的反复训练达到温度补偿目的。引入Logistic映射法与混沌扩频序列法,计算混沌系数间的互协方差函数,采用M-N-L结构的前馈网络对原BP网络的三层建构展开优化,并以此提高温度误差模糊控制的精度。测试结果表明:方法对传感器温度误差的提取值与实际超差值的差距低于0.03,收敛步数小于120步,误差补偿后误差比率低于19.3%,残差平方和低于0.23,对传感器温度误差的控制更加精准,温度补偿的效率更高,提高了误差控制效果。展开更多
文摘Serveral methods for the pararrel acquisition of a PN sequence in a baseband direct sequence spread spectrum system are investigated. Four different kinds of schemes are considered: the optimal estimation scheme, the locally optimal estimation scheme, the optimal testing searches and the locally optimal testing scheme. In the four kinds of parallel acquisition schemes, the expressions for the probability of error are given and compared with the actual error probabilities obtained via Monte Carlo simulation. We also outline a technique that can be suboptimal because of a large amount of hardware and computation when using the parallel acquisition schemes.
文摘为了保证数字温度传感器对温度的准确读取,需要对传感器温度误差展开有效控制。为了提高数字温度传感器温度误差控制效果,提出基于混沌误差反向传播(Error Back Propagation Training,BP)神经网络算法的数字温度传感器温度误差模糊控制方法。首先利用经验模态分解法对收集到的温度测量数据展开去噪处理,计算不同温度区间内的温度测量超差概率,进而实现误差特征阈值的提取;建立三层神经网络,通过对温度误差的反复训练达到温度补偿目的。引入Logistic映射法与混沌扩频序列法,计算混沌系数间的互协方差函数,采用M-N-L结构的前馈网络对原BP网络的三层建构展开优化,并以此提高温度误差模糊控制的精度。测试结果表明:方法对传感器温度误差的提取值与实际超差值的差距低于0.03,收敛步数小于120步,误差补偿后误差比率低于19.3%,残差平方和低于0.23,对传感器温度误差的控制更加精准,温度补偿的效率更高,提高了误差控制效果。
