MATLAB의 고정소수점 행렬 연산
다음 함수를 사용하여 고정소수점 행렬 연산을 수행하고 효율적인 C/C++ 코드를 생성합니다. 이러한 함수는 임베디드 장치에 효율적인 CORDIC 기반 알고리즘을 사용하여 선형 연립방정식의 해를 구하고 핵심 행렬 연산(예: QR 분해 및 특이값 분해)을 수행합니다. 포함된 Fixed-Point Designer™ 함수를 사용하여 선형 시스템 솔버와 행렬 분해 블록을 위한 최적의 고정소수점 데이터형을 해석적으로 결정합니다.
이러한 알고리즘의 Simulink® 블록 구현은 Simulink에서의 고정소수점 행렬 연산 항목을 참조하십시오. MATLAB® 및 Simulink에서 수학 연산에 대한 CORDIC 기반 구현과 다른 임베디드 효율적인 구현은 MATLAB 및 Simulink에서의 고정소수점 수학 연산 항목을 참조하십시오.
함수
도움말 항목
- Determine Fixed-Point Types for QR Decomposition
Use
fixed.qrFixedpointTypesto determine fixed-point types for computation of QR decomposition. - Determine Fixed-Point Types for Q-less QR Decomposition
Use
fixed.qlessqrFixedpointTypesto determine fixed-point types for computation of Q-less QR decomposition. - Algorithms to Determine Fixed-Point Types for Complex Q-less QR Matrix Solve A'AX=B
Derivation of algorithms for determining fixed-point types for complex Q-less QR matrix solve.
- Determine Fixed-Point Types for Complex Q-less QR Matrix Solve A'AX=B
Use
fixed.complexQlessQRFixedpointTypesto determine fixed-point types for computation of the complex least-squares matrix equation. - Determine Fixed-Point Types for Complex Q-less QR Matrix Solve with Tikhonov Regularization
This example shows how to use the
fixed.complexQlessQRMatrixSolveFixedpointTypesfunction to analytically determine fixed-point types for the solution of the complex least-squares matrix equation - Algorithms to Determine Fixed-Point Types for Complex Least-Squares Matrix Solve AX=B
Derivation of algorithms for determining fixed-point types for complex QR matrix solve.
- Determine Fixed-Point Types for Complex Least-Squares Matrix Solve AX=B
Use
fixed.complexQRFixedpointTypesto determine fixed-point types for computation of the complex least-squares matrix equation. - Tikhonov 정규화를 사용하여 복소수 최소제곱 행렬 해의 고정소수점 데이터형 결정하기
이 예제에서는
fixed.complexQRMatrixSolveFixedpointTypes함수를 사용하여 복소수 최소제곱 행렬 방정식 해의 고정소수점 데이터형을 해석적으로 결정하는 방법을 보여줍니다. - Algorithms to Determine Fixed-Point Types for Real Q-less QR Matrix Solve A'AX=B
Derivation of algorithms for determining fixed-point types for real Q-less QR matrix solve.
- Determine Fixed-Point Types for Real Q-less QR Matrix Solve A'AX=B
Use
fixed.realQlessQRFixedpointTypesto determine fixed-point types for computation of the real least-squares matrix equation. - Determine Fixed-Point Types for Real Q-less QR Matrix Solve with Tikhonov Regularization
This example shows how to use the
fixed.realQlessQRMatrixSolveFixedpointTypesfunction to analytically determine fixed-point types for the solution of the real least-squares matrix equation - Algorithms to Determine Fixed-Point Types for Real Least-Squares Matrix Solve AX=B
Derivation of algorithms for determining fixed-point types for real least-squares matrix solve.
- Determine Fixed-Point Types for Real Least-Squares Matrix Solve AX=B
Use
fixed.realQRMatrixSolveFixedpointTypesto determine fixed-point types for computation of the real least-squares matrix equation. - Determine Fixed-Point Types for Real Least-Squares Matrix Solve with Tikhonov Regularization
This example shows how to use the
fixed.realQRMatrixSolveFixedpointTypesfunction to analytically determine fixed-point types for the solution of the real least-squares matrix equation - Compute Forgetting Factor Required for Streaming Input Data
Use
fixed.forgettingFactorandfixed.forgettingFactorInverseto compute forgetting factor. - Estimate Standard Deviation of Quantization Noise of Complex-Valued Signal
Use
fixed.complexQuantizationNoiseStandardDeviationto estimate standard deviation of quantization noise. - Estimate Standard Deviation of Quantization Noise of Real-Valued Signal
Use
fixed.realQuantizationNoiseStandardDeviationto estimate standard deviation of quantization noise.
추천 예제
Perform QR Factorization Using CORDIC
Write MATLAB code that works for both floating-point and fixed-point data types. The algorithm used in this example is the QR factorization implemented via CORDIC.
