Location:
Search - IMM C
Search list
Description: c program
Platform: |
Size: 3639 |
Author: 罗华 |
Hits:
Description: c program
Platform: |
Size: 3072 |
Author: 罗华 |
Hits:
Description: C++primmer,一本优秀的C++提高书籍,值得一看.-C primmer, an outstanding increase of C books, an eye-catcher.
Platform: |
Size: 81920 |
Author: 一颗红星 |
Hits:
Description: 雷达数据处理的重要模型算法之一，该代码对imm算法的不同参数下进行了详细的仿真。-Radar Data Processing, one of the important model algorithm, the code imm algorithm under different parameters of the simulation in detail.
Platform: |
Size: 8192 |
Author: 董 |
Hits:
Description: 基于数据融合技术的多目标跟踪算法研究(Kalman, Imm.etc)-Motion image sequences of multi-target tracking and implementation
Platform: |
Size: 145408 |
Author: jpj |
Hits:
Description: ALU
指令格式(16位） op DR SR fun
0--3 4—7 8--11 12--15
指令类 OP码 指令 FUN 功能描述
控制 0000 NOP 0000 空指令
HLT 0001 停机
有条件跳转 0010 JZ 0000 Z=1，跳转
JC 0001 C=1，跳转
JNC 0010 C=0，跳转
JNZ 0100 Z=0，跳转
Jump 0101 无条件跳转
LOAD 0011 [SR]->DR
STORE 0100 SR->[DR]
MOV(reg to reg) 0101 SR->DR
MOV(IMM to reg) 0110 IMM->DR
移位 0111 SHL 0000 逻辑左移/算术左移
SHR 0001 逻辑右移
SAR 0010 算术右移
RCL 0011 含进位的左循环移位
RCR 0100 含进位的右循环移位
ROL 0101 不含进位左循环移位
ROR 0111 不含进位右循环移位
算术类 1000 ADD 0000 DR+SR->DR
SUB 0001 DR-SR->DR
MUL 0010 DR*SR->DR,SR
DEC 0011 DR+1->DR
INC 0100 DR-1->DR
CMP 1000 DR-SR,比较
逻辑类 1001 AND 0000 DR and SR->DR
OR 0001 DR or SR->DR
XOR 0010 DR xor SR->DR
NOT 0100 /DR->DR
TEST 1000 DR and SR 测试
栈类 1010 POP 0000 DR入栈
PUSH 0001 出栈->DR
-It was writen by myself,and it is very easy!
Platform: |
Size: 1024 |
Author: 翟志强 |
Hits:
Description: Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
Design a fuzzy control system to
This is a book about fuzzy system
Cloud adaptive genetic algorithm
BP neural network and RBF neural
use a three-layers BP network to
Professor Lin in Taiwan support v
neural network theory
Practical matlab time series ar m
efficient robot path planning-pub
A population-based artificial imm
A description of the neural netwo
Clustering AHC, K-means, SOM--Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
Design a fuzzy control system to
This is a book about fuzzy system
Cloud adaptive genetic algorithm
BP neural network and RBF neural
use a three-layers BP network to
Professor Lin in Taiwan support v
neural network theory
Practical matlab time series ar m
efficient robot path planning-pub
A population-based artificial imm
A description of the neural netwo
Clustering AHC, K-means, SOM-NN
Platform: |
Size: 91136 |
Author: mohsen |
Hits:
Description: Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
Design a fuzzy control system to
This is a book about fuzzy system
Cloud adaptive genetic algorithm
BP neural network and RBF neural
use a three-layers BP network to
Professor Lin in Taiwan support v
neural network theory
Practical matlab time series ar m
efficient robot path planning-pub
A population-based artificial imm
A description of the neural netwo
Clustering AHC, K-means, SOM--Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
Design a fuzzy control system to
This is a book about fuzzy system
Cloud adaptive genetic algorithm
BP neural network and RBF neural
use a three-layers BP network to
Professor Lin in Taiwan support v
neural network theory
Practical matlab time series ar m
efficient robot path planning-pub
A population-based artificial imm
A description of the neural netwo
Clustering AHC, K-means, SOM-NN
Platform: |
Size: 13312 |
Author: mohsen |
Hits:
Description: CGSVD Compact generalized SVD of a matrix pair in regularization problems.
sm = cgsvd(A,L)
[U,sm,X,V] = cgsvd(A,L) , sm = [sigma,mu]
Computes the generalized SVD of the matrix pair (A,L):
[ A ] = [ U 0 ]*[ diag(sigma) 0 ]*inv(X)
[ L ] [ 0 V ] [ 0 eye(n-p) ]
[ diag(mu) 0 ]
where
U is m-by-n , sigma is p-by-1
V is p-by-p , mu is p-by-1
X is n-by-n .
It is assumed that m >= n >= p, which is true in regularization problems.
Reference: C. F. Van Loan, Computing the CS and the generalized
singular value decomposition , Numer. Math. 46 (1985), 479-491.
Per Christian Hansen, IMM, 12/19/97.
Initialization. - CGSVD Compact generalized SVD of a matrix pair in regularization problems.
sm = cgsvd(A,L)
[U,sm,X,V] = cgsvd(A,L) , sm = [sigma,mu]
Computes the generalized SVD of the matrix pair (A,L):
[ A ] = [ U 0 ]*[ diag(sigma) 0 ]*inv(X)
[ L ] [ 0 V ] [ 0 eye(n-p) ]
[ diag(mu) 0 ]
where
U is m-by-n , sigma is p-by-1
V is p-by-p , mu is p-by-1
X is n-by-n .
It is assumed that m >= n >= p, which is true in regularization problems.
Reference: C. F. Van Loan, Computing the CS and the generalized
singular value decomposition , Numer. Math. 46 (1985), 479-491.
Per Christian Hansen, IMM, 12/19/97.
Initialization.
Platform: |
Size: 1024 |
Author: 常丁月 |
Hits: