5 Days of Matlab, Simulink & SimScape + ChatGPT - New!

Learn to build a mass–spring–damper model using MATLAB live script and Simscape, drag-and-drop components, and simulate responses to a force input.

Build your first mass-spring-damper model in simscape by dragging and dropping components in simulink, applying a step input to a 1 kg mass, and observing displacement X.

demonstrates simulating a mass–spring–damper system in simulink with simscape, using to workspace blocks and a matlab live script to control m, b, k, and F, and plot displacement and force.

Develop a two degrees of freedom mechanical system in Simscape with two masses, two springs, and dampers, apply a unit step force, and plot x1, x2, and force.

Build and simulate a two-mass translational mass-spring-damper system in Simulink from library blocks, add an ideal force input, two sensors, and data logging for x1, x2 and F.

Explore a two-degree-of-freedom mass-spring-damper system in MATLAB and Simscape, adjusting damping and spring parameters with sliders, simulating a unit-step input, and visualizing displacements x1, x2 and input force.

Derive mass–spring–damper equations from Newton's second law and model them in Simulink using a free body diagram. Build and simulate with MATLAB live script for the capstone project.

Draw the free body diagram for a mass-spring-damper system and derive its differential equation using Newton's second law; prepare a Simulink-ready model to simulate displacement under a force.

Build a Simulink model of a mass spring damper. Derive x double dot equals (F - B x dot - K x)/M via integrators for x dot and x.

Build a mass-spring-damper model in Simulink by dragging blocks, setting gains k and b, and driven by force through two integrators to produce x and x dot, displayed on scope.

Simulate a mass spring damper model in Simulink using the MATLAB live editor, adjusting damping, mass, and spring constants. Plot displacement and input force to verify the system response.

Examine a two-mass, two-spring, two-damper system; draw the free body diagram, derive the differential equations, and simulate in Simulink, plotting x1, x2, and the input force under a unit step.

This capstone solution walks through a two-mass, two-spring, two-damper system with displacements x1 and x2, derives the free-body diagrams and motion equations, and previews building a Simulink model for simulation.

Continue solving the final capstone with a two-mass model, derive equations for x1 and x2, add k2 and b2 terms, and build and simulate the Simulink model.

Demonstrate building and simulating a mass–spring–damper model in Simulink, translating free-body diagrams into coupled differential equations and plotting results in scope and workspace.

Explore the mass-spring-damper system in Simulink by simulating two mass spring damper systems with adjustable damping and a force input, and review the capstone solution with outputs x1 and x2.

Learn to model mechanical systems in the Laplace domain, convert time-domain dynamics to algebraic transfer functions, and build Simulink models of a mass spring damper system.

Learn how to model a mass-spring-damper system using the Laplace transform, moving from the time domain to the s-domain and using a transform table for quick analysis.

Compute the Laplace transform of f(t)=e^{-alpha t} to get F(s)=1/(s+alpha) by substituting into the Laplace definition and evaluating the improper integral.

Learn to derive the transfer function of a mass–spring–damper system by applying Laplace transforms, assuming zero initial conditions, to obtain X(s)/F(s) = 1/(M s^2 + B s + K).

Build a Simulink transfer function model in the SX domain using a step input to verify the mass-spring-damper transfer function, configure coefficients, and save outputs for analysis.

Simulate a mass-spring-damper system using transfer functions in Simulink, with MATLAB live script for defining parameters and plotting input force and displacement.

Explain the final capstone overview of a two-degree-of-freedom mass-spring-damper system, derive transfer functions x1(s)/F(s) and x2(s)/F(s), and build the model in Simulink with the given parameters.

Derives a two-mass, two-displacement mass-spring-damper model, develops free-body diagrams and Newton's second law of motion equations, and applies Laplace transforms to obtain transfer functions x1(s)/F and x2(s)/F.

Continuing the final capstone project, derive two transfer functions for a two-mass system using Newton's laws and Laplace transforms, then prepare Simulink models in MATLAB.

Build a Simulink model by embedding two transfer functions into blocks, configuring their s-domain coefficients, and wiring an input force to produce displacement outputs for capstone project solution part 3.

Simulate two transfer functions in a Simulink mass-spring-damper model with a unit step input, revealing a compact transfer function and how damping 0.3 or 0 yields oscillations or perpetual motion.

Combine transfer functions, time-domain, and physical modeling in a single Simulink model to validate that three strategies yield the same outputs.

Build and compare strategies for Simulink and Simscape models of a mass-spring-damper system in the time domain, using transfer functions and unit step input, and display outputs on a scope.

Simulate the mass-spring-damper model using three strategies, run a sanity check, and compare outputs to verify they align on top of each other.

Show a degrees-of-freedom mechanical system in Simulink using three transfer-function techniques and time-domain analysis. Plot x1, x2, and input force for all three strategies on one graph to verify consistency.

Demonstrate the final capstone solution by comparing three representations of a two-mass mass-spring-damper system under one input, using Simulink, Simscape, and transfer functions.

Simulate the mass-spring-damper system in Simulink using three representations—transfer function, the same escape, and the time-domain representation—and compare outputs x1 and x2 with plots.

Learn the basics of artificial intelligence and machine learning in MATLAB, apply deep learning to health care, and train neural networks to detect diabetes and cancer using real data.

Understand the problem statement and business case for a diabetes prediction project using MATLAB. Outline inputs like pregnancies, glucose, bmi, blood pressure, insulin, and age with output 0 or 1.

Import datasets from diabetes.csv in MATLAB, convert to arrays, separate inputs (age, insulin, bmi, glucose, blood pressure) and output (diabetes outcome), preparing data for neural network training.

Explore the intuition of artificial intelligence and deep learning by comparing biological and artificial neurons, detailing weighted inputs, activation, and how deep neural networks learn with MATLAB.

Learn the AI training process: split data 80/20 for training and testing, initialize random weights, update via optimizer, and evaluate a trained, frozen network on inputs like cat vs dog.

Train a deep neural network in MATLAB with patternnet to classify diabetes, using a feedforward network and a hidden layer, then train over epochs and evaluate on unseen data.

Explore classification model evaluation with confusion matrices, true positives, true negatives, false positives, false negatives, roc curves, and auc for training, testing, and validation data.

Assess the trained diabetes classifier by reviewing KPIs, confusion matrices, ROC curves, and AUC across training, validation, and test sets. Compare how changing hidden layers impacts accuracy and ROC performance.

Train a deep neural classifier in MATLAB on the breast cancer dataset to distinguish malignant from benign tumors, using EDA, 80/20 split, confusion matrices, and ROC analysis.

Develop a deep pattern net classifier for breast cancer using a 30-feature dataset, training a multi-layer neural network and evaluating with the ROC curve and confusion matrix.

Use ChatGPT and GPT-4 to find and copy syntax across languages, with Python examples like for loops, conditionals, variables, and data types, plus libraries like pandas and matplotlib.

Create a one-week or one-month study plan with ChatGPT for Python, outlining daily goals and recommended books, courses, and key topics like basics and data science.

Demonstrate how ChatGPT generates and tests code across languages using functions, calls, and user input. Learn cross-language translation and concise one-liners for Python, MATLAB, Swift, C++, and JavaScript.

Learn how to debug code with ChatGPT, identify and fix syntax errors, indentation issues, and common programming mistakes in Python, JavaScript, and data science workflows.

Demonstrate practical Python optimization tips with ChatGPT, including built-in functions, list comprehension, type hints, and memory-efficient data handling, plus ML pipeline tuning with grid search CV.

Use ChatGPT to perform code reviews, assess readability, security, and errors, and suggest improvements like doc strings, robust error handling, and Python formatting (pep8).

Leverage ChatGPT to add features to Python code, from user input and arithmetic extension to multi-input handling, data visualization with matplotlib, and swapping machine learning algorithms.

Explore using chatgpt to write unit tests, assess code coverage, and perform fault injection and regression testing in a practical ml workflow.

Learn to document code using ChatGPT, generating docstrings and comments for Python functions and scripts, with practical prompts and examples like factorial, plots, and machine learning scripts.