Predictive Control for Multi-Input Multi-Output (MIMO) Systems
MATLAB Simulation of Multi-Input Multi-Output (MIMO) Predictive Control with Implementation Details
MIMO Refers to Multiple-Input Multiple-Output
MIMO (Multiple-Input Multiple-Output) technology can be broadly classified into two categories: transmit/receive diversity and spatial multiplexing. Traditional multi-antenna systems are used to enhance diversity gain for mitigating channel fading, where signals carrying identical information are transmitted via different paths. The receiver obtains multiple independently faded copies of data symbols, thereby achieving higher reception reliability. This technique is typically implemented using multi-antenna configurations, which have been extensively studied in mobile communications. From a coding perspective, diversity techniques often employ Alamouti coding schemes to orthogonalize transmission paths, while spatial multiplexing algorithms like Zero-Forcing or MMSE detectors separate layered data streams at the receiver.
MIMO Channel Simulation
A multiple-input multiple-output channel simulation program that can be extended to simulate various scenarios, serving as a foundational template with customizable parameters and adaptable architecture
Decoupling Control of Multi-Input Multi-Output Systems Using PID Neural Networks
Decoupling Control of Multi-Input Multi-Output Systems via PID Neural Networks with Implementation Insights
Multi-Input Multi-Output Support Vector Regression Machine
Source code implementation for a Multi-Input Multi-Output Support Vector Regression (MIMO SVR) machine, addressing the limitation of single-output in traditional Support Vector Machines
Time Domain Experimental Modal Analysis for Structures
This software specializes in time domain experimental modal analysis for structures, supporting single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) program designs. Unlike traditional modal analysis software based on frequency domain methods requiring Fourier transforms of sampled signals, our implementation processes measured time-domain signals directly. This approach eliminates adverse effects from signal truncation such as spectral leakage, side lobes, and low resolution that impact identification accuracy. The algorithm implementation reduces computational time and enables real-time online parameter identification for structures under actual operating conditions, providing more efficient analysis capabilities compared to conventional Fourier-based methods.
Multi-Input Multi-Output Time Domain Modal Parameter Identification Method Using ERA
This paper presents a multi-input multi-output time domain modal parameter identification method utilizing the Eigensystem Realization Algorithm (ERA) with enhanced computational accuracy and robustness for dynamic system characterization.
MIMO-OFDM Multiuser Detection Simulation Program (MATLAB Implementation)
MATLAB-based simulation program for Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) multiuser detection systems
Application of Fuzzy Controller Control Algorithm in Robot Obstacle Avoidance Systems
This control algorithm implements a fuzzy controller for robotic obstacle avoidance systems, designed as a multi-input multi-output control system, with simulation results closely matching real-world performance.
Multi-Input Multi-Output Wavelet Network Model
Multi-Input Multi-Output Wavelet Network Model based on the Journal of Ocean University of Qingdao paper implementation, requires MATLAB Neural Network Toolbox installation, features embedded initialization algorithm development with detailed program documentation for optimal usability