搜索结果: 1-15 共查到“知识库 参数估计”相关记录330条 . 查询时间(6.343 秒)
Focusing on identification, support recovery, and parameter estimation of multivariate ARMA systems with exogenous inputs (ARMAX) is considered. It is assumed that sparsity appears not only in the unk...
多尺度量子谐振子优化算法的并行性研究
多尺度量子谐振子优化算法 算法并行性 加速比 并行粒度 函数优化
2016/12/3
多尺度量子谐振子优化算法(MQHOA, multi-scale quantum harmonic oscillator algorithm)是一种利用量子谐振子波函数构造的新的智能算法，采样运算是MQHOA算法的基本运算单元和主要运算量，采样运算的独立性赋予MQHOA算法内在并行性。通过对MQHOA算法群体参数和采样参数进行实验，确定算法的并行粒度并提出多尺度量子谐振子并行算法（MQHOA-P, ...
Gain Fixed Pattern Noise Correction via Optical Flow
Digital camera CMOS image sensor Fixed pattern noise (FPN) Optical fl ow estimation gain FPN CDS
2015/8/17
Fixed pattern noise (FPN) or nonuniformity caused by device and interconnect parameter variations across an image sensor is a major source of image quality degradation especially in CMOS image sensors...
Photocurrent Estimation for a Self-Reset CMOS Image Sensor
CMOS image sensor self-reset SNR dynamic range estimation
2015/8/17
CMOS image sensors are capable of very high frame rate non-destructive readout. This capability and the potential of integrating memory and signal processing with the sensor on the same chip enable th...
Optical Flow estimation using high frame rate sequences
Optical Flow estimation high frame rate sequences
2015/8/17
Gradient-based optical flow estimation methods such as Lucas Kanade method work well for scenes with small dlsplacements but fail when objects move with large displacements. Hierarchical matching-base...
Photocurrent Estimation from Multiple Non-destructive Samples in a CMOS Image Sensor
Photocurrent Estimation Multiple Non-destructive Samples CMOS Image Sensor
2015/8/17
CMOS image sensors generally su er from lower dynamic range than CCDs due to their higher readout noise.Their high speed readout capability and the potential of integrating memory and signal processin...
On parameter convergence in adaptive control
Control parameter the parameter error signal and reference signal
2015/8/13
It is well known that the parameter error as well as the model-plant mismatch error in a model reference adaptive scheme tends exponentially to zero iff a certain sufficient richness condition holds f...
Necessary and sufficient conditions for parameter convergence in adaptive control
Adaptive control adaptive systems identifiability parameter estimation
2015/8/13
Using Generalized Harmonic Analysis, a complete description of parameter convergence in Model Reference Adaptive Control (MRAC) is given in terms of the spectrum of the exogenous reference input signa...
Likelihood bounds for constrained estimation with uncertainty
Estimate the linear model model uncertainty likelihood estimation the second-order cone programming
2015/8/10
This paper addresses the problem of finding bounds on the optimal maximum a posteriori (or maximum likelihood) estimate in a linear model under the presence of model uncertainty. We introduce the nove...
Embedded estimation of fault parameters in an unmanned aerial vehicle
Based on the model of fault plane convex optimization problem constraint quadratic programming the horizon framework
2015/8/10
In this paper, we present a model-based approach for estimating fault conditions in an aircraft. We formulate fault estimation as a convex optimization problem, where estimates are obtained by solving...
一种双参数可调的水声信道自适应均衡算法
信息处理技术 浅海水声通信 自适应均衡 双参数平行滤波器组算法
2015/4/10
针对浅海水声信道固有的随机时-空-频变、高噪、强多径等特性及变化的多径时延扩展,在变步长最小均方(LMS)平行滤波器组(PFB-LMS)算法的基础上提出了一种新的水声通信自适应均衡算法。该算法将变阶数和变步长的调整结合起来,降低了算法对迭代步长和均衡器阶数的敏感度。仿真结果表明,新算法在参数适应性方面优于传统LMS 及PFB-LMS 算法。
为了给出一种基于Duffing系统的完整信号检测与估计方法,本文采用了耦合型间歇混沌振子,并引入了变尺度方法,以实现仅用单个Duffing振子完成对未知频率具有任意出相位的正弦信号的检测与频率估计.此外本文还提出了一种新的正弦信号幅值、相位同步估计方法,并综合方法原理和仿真实验结果证明了此方法具有估计精度高、抗噪声干扰能力强和实时性好的优点.最后用一组仿真实验结果验证了本文所提出的检测方法的有效性...
针对一类带丢包的Markov切换系统,提出一种含有双Markov切换参数的交互式多模型算法.该算法利用一个二态的Markov链对系统是否丢包进行建模,得到双Markov链系统,通过定义乘积集将两个Markov切换参数所对应的模型集进行融合,并给出单个模型集中各模型与乘积集中各模型的对应关系.在此基础上,以交互式多模型算法为框架,采用分层的方法,并利用一种新的最优估计算法对双Markov链系统进行滤...
基于N阶近邻路网的车辆行程时间估计模型
行程时间预测 隐马尔科夫模型 聚类
2017/1/4
车联网的提出为智能交通的研究提供了新的交通信息收集技术.针对短时交通中车辆的路网行程时间估计问题,提出了基于N阶近邻的隐马尔科夫模型,利用马尔科夫性质来解决道路行程时间的前后关联性问题,同时考虑不同道路的异构性构建了N阶近邻路网模型来模拟路网间的交互影响.针对短时交通中实时数据更新的问题,提出基于道路关联性算法,并结合车联网的采集技术给出了迭代更新模型的方法.实验表明,本文提出的方法在短时交通车辆...