Program for Model Reference Adaptive Control Based on Gradient Method (MIT-MRAC)
Implementation of Model Reference Adaptive Control (MRAC) using the MIT rule gradient descent approach for uncertain system adaptation
MATLAB Implementation of Optimization Algorithms - Golden Section, Gradient Descent, Conjugate Gradient, and Penalty Function Methods
Self-developed MATLAB optimization algorithms including Golden Section (0.618) method, Gradient Descent, Conjugate Gradient, and Penalty Function approaches with detailed implementation code and algorithmic explanations.
Three MATLAB Radial Basis Function Neural Network Programs: Cluster-Based, Gradient-Based, and OLS-Based Implementations
Three MATLAB implementations of Radial Basis Function (RBF) neural networks featuring distinct training methodologies: cluster-based RBF, gradient descent-based RBF, and Orthogonal Least Squares (OLS)-based RBF networks with detailed algorithmic descriptions and code structure explanations.
Gradient Method for Displacement Solving
Implementing gradient method for displacement calculation with code optimization
Seven RBF Neural Networks: Source Code Collection with Implementation Approaches
Source code implementations for seven RBF neural networks featuring gradient-based methods, OLS (Ordinary Least Squares), clustering algorithms, k-means clustering, and function approximation techniques for network design and predictive modeling
Constrained Optimization Method – Steepest Descent Method (Also Known as Gradient Method)
Constrained Optimization Method – Steepest Descent Method (also called Gradient Method) is one of the earliest approaches developed for solving extremum problems of multivariable functions. This iterative algorithm utilizes gradient information to locate local minima through directional updates, with implementations often involving step size selection and convergence criteria.
Gradient-Based RBF Neural Network Implementation
An RBF neural network program implemented using gradient descent method, designed for approximating and fitting input data patterns with optimization capabilities.
MATLAB Implementation of Gradient Methods, Interior Point Methods, Exterior Point Methods, Penalty Functions, and Linear Gradient Approach
MATLAB source codes for gradient methods, interior point methods, exterior point methods, penalty functions, and linear gradient optimization algorithms - ready to run with guided input parameters
Comparison of Variational Methods in Optimal Control: Newton's Method, Gradient Descent, and Conjugate Gradient
This semester's optimal control assignment compares three variational methods—Newton's Method, Gradient Descent, and Conjugate Gradient—with enhanced algorithm explanations and code implementation insights.
MATLAB Implementation of Fingerprint Orientation Field
MATLAB code implementation for fingerprint orientation field calculation with five primary methods and algorithm analysis