代码搜索：predict
找到约 2,271 项符合「predict」的源代码
代码结果 2,271
www.eeworm.com/read/162313/10315759
m ekf_3dcv_filter.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;
www.eeworm.com/read/272822/10943007
m ekf.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;
www.eeworm.com/read/481758/6637993
m inamkalman.m
%**************************************
%%%% KALAMAN FILTER %%%%%
%**************************************
clear all
close all
A=[1];
H=[1];
Q=input('process noise covarinace = ');
www.eeworm.com/read/150263/12301545
m ekf_3dcv_filter.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;
www.eeworm.com/read/338384/12310580
m ekf_3dcv_filter.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;
www.eeworm.com/read/295526/8156076
m zbhj.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1; % 采样周期
hits=2000; % 采样点数
MCNum=10; % Monte Ca
www.eeworm.com/read/412485/11197876
m ekf_3dcv_filter.m
%参考文献
%Decoupling joint probabilistic data association algorithm for multiple target tracking
%杂波环境下多传感器的数据融合
%三维常速CV模型
clear all;
clc;
T=1;
www.eeworm.com/read/142437/12945047
am makefile.am
lib_LTLIBRARIES = libfaad.la
include_HEADERS = $(top_srcdir)/include/faad.h \
$(top_srcdir)/include/neaacdec.h
libfaad_la_LDFLAGS = -lm
libfaad_la_SOURCES = bits.c cfft.c decoder.
www.eeworm.com/read/132649/5913733
lib makefile.gui_algo_basic.lib
#
# ===========================================================================
# PRODUCTION $Log: Makefile.gui_algo_basic.lib,v $
# PRODUCTION Revision 1000.3 2004/06/01 20:54:44 gouriano
# PR
www.eeworm.com/read/429501/8804975
m gmpred.m
function [Predict]=gmpred(x,N)
m=N-1+length(x);
n=length(x);
sum0=0;
x1=[];
for i=1:n
sum0=sum0+x(i);
x1=[x1 sum0];
end
for i=1:n-1
b(i)=-(x1(i)+x1(i+1))/2;
Yn(i)=x(i+1);
e