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❇️پروژه درس شناسایی سیستم
—شناسایی سیستم به صورت فضای حالت
—شناسایی سیستم با مدل های ARX , OE , BJ
—شناسایی غیر خطی NLARX ,...
—شناسایی جعبه خاکستری
—بهبود مدل با استفاده از همرشناین وینر
—شناسایی حوزه زمان سیستم با تولباکس شناسایی سیسیتم
—طراحی کنترل کننده پیش بین برای مدل شناسایی شده
—مقایسه کنترل کننده پیش بین با کنترل کننده PID همراه با نویز در خروجی و ورودی سیستم و ردیابی ورودی مرجع
—راهکاری ساده برای فیلتر نویز و...
دانلود:
https://npd.servr.ir/ident/pro/
🆔 @MATLAB_House
@MATLABHOUSE
#MATLABCoding #ControlSystems #SystemIdentification #Modeling #NonlinearIdentification #PredictiveControl #PIDController #NoiseFiltering #Simulation #MATLABTutorial
—شناسایی سیستم به صورت فضای حالت
—شناسایی سیستم با مدل های ARX , OE , BJ
—شناسایی غیر خطی NLARX ,...
—شناسایی جعبه خاکستری
—بهبود مدل با استفاده از همرشناین وینر
—شناسایی حوزه زمان سیستم با تولباکس شناسایی سیسیتم
—طراحی کنترل کننده پیش بین برای مدل شناسایی شده
—مقایسه کنترل کننده پیش بین با کنترل کننده PID همراه با نویز در خروجی و ورودی سیستم و ردیابی ورودی مرجع
—راهکاری ساده برای فیلتر نویز و...
دانلود:
https://npd.servr.ir/ident/pro/
🆔 @MATLAB_House
@MATLABHOUSE
#MATLABCoding #ControlSystems #SystemIdentification #Modeling #NonlinearIdentification #PredictiveControl #PIDController #NoiseFiltering #Simulation #MATLABTutorial
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❇️Mastering Optimization with Slime Mould Algorithm: A MATLAB Tutorial
Dive into our MATLAB tutorial on the Slime Mould Algorithm (SMA) for stochastic optimization. Learn how SMA, inspired by nature, addresses complex optimization problems. This video covers SMA's basics, its MATLAB implementation, and showcases its effectiveness with visualizations and examples, catering to both beginners and experts. Ideal for researchers, students, and enthusiasts in computational intelligence, this tutorial is designed to enrich your optimization knowledge and spark innovation.
🔻YouTube: https://youtu.be/FqDkJSRGBiU
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE
#SlimeMouldAlgorithm #OptimizationTutorial #MATLABCoding #StochasticOptimization #AlgorithmVisualisation #ComputationalIntelligence #MATLABTutorial #EngineeringEducation #ScienceAndTechnology #ResearchInnovation
Dive into our MATLAB tutorial on the Slime Mould Algorithm (SMA) for stochastic optimization. Learn how SMA, inspired by nature, addresses complex optimization problems. This video covers SMA's basics, its MATLAB implementation, and showcases its effectiveness with visualizations and examples, catering to both beginners and experts. Ideal for researchers, students, and enthusiasts in computational intelligence, this tutorial is designed to enrich your optimization knowledge and spark innovation.
🔻YouTube: https://youtu.be/FqDkJSRGBiU
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE
#SlimeMouldAlgorithm #OptimizationTutorial #MATLABCoding #StochasticOptimization #AlgorithmVisualisation #ComputationalIntelligence #MATLABTutorial #EngineeringEducation #ScienceAndTechnology #ResearchInnovation
👍2
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🔰Linear Control Training Workshop - Session 2
🔹Partial Fraction Expansion: Learn how to use the residue() command to easily perform partial fraction expansion on transfer functions. See examples of expanding proper and improper rational functions.
🔸Transforming Mathematical Models: Discover how to convert between different representations of dynamic systems using commands like tf2ss, ss2tf, zp2tf, etc. Examples show conversions between transfer functions, state-space models, pole-zero form, and discrete-time systems.
🔹Block Diagram Modeling: Master the techniques for representing interconnected systems with transfer function or state-space blocks. Learn the MATLAB syntax for series, parallel, and feedback connections. See how to extract the overall transfer function or state-space model.
🔸Telegram:
🆔Channel: @MATLAB_House
🆔Group:@MATLABHOUSE
#MATLAB #ControlSystems #DynamicSystems #TransferFunctions #StateSpace #BlockDiagrams #ModelConversion #PartialFractions #MATLABTutorial #ModelingAndAnalysis
🔹Partial Fraction Expansion: Learn how to use the residue() command to easily perform partial fraction expansion on transfer functions. See examples of expanding proper and improper rational functions.
🔸Transforming Mathematical Models: Discover how to convert between different representations of dynamic systems using commands like tf2ss, ss2tf, zp2tf, etc. Examples show conversions between transfer functions, state-space models, pole-zero form, and discrete-time systems.
🔹Block Diagram Modeling: Master the techniques for representing interconnected systems with transfer function or state-space blocks. Learn the MATLAB syntax for series, parallel, and feedback connections. See how to extract the overall transfer function or state-space model.
🔸Telegram:
🆔Channel: @MATLAB_House
🆔Group:@MATLABHOUSE
#MATLAB #ControlSystems #DynamicSystems #TransferFunctions #StateSpace #BlockDiagrams #ModelConversion #PartialFractions #MATLABTutorial #ModelingAndAnalysis