Matlab Implementation of Gradient-Based ICA Algorithm

基于LSB的图像水印算法是通过MATLAB实现的，包含有实现的源代码和论文。该算法通过最低有效位（LSB）技术，嵌入水印信息于图像中，确保视觉质量不受影响，同时实现信息的隐藏与传输。

This MATLAB example demonstrates the use of gradient descent to iteratively solve for the coefficients of a noisy quadratic curve. The algorithm is applied to fit a quadratic curve model, and the noisy data points are used to estimate the optimal coefficients through gradient descent optimization. This example is designed to inspire and help others understand how gradient descent can be applied in real-world curve fitting problems.

本论文针对BP算法，即当前前馈神经网络训练中应用最多的算法进行改进，并在MATLAB中实现。

该项目提供了基于梯度下降的softmax回归实现，专注于MNIST数据分类。此外，还包含多个领域的Matlab仿真代码，涵盖智能优化算法、神经网络预测、信号处理等应用。

Golden Section Search Algorithm Overview of the Algorithm The Golden Section Search algorithm is an optimization technique used to find the extremum (maximum or minimum) of a unimodal function within a specified interval. It leverages the golden ratio to reduce the search interval step-by-step, ensuring efficient convergence. Steps of the Algorithm Initialize two points within the interval [a, b] using the golden ratio. Evaluate the function at these two points. Compare the function values and update the interval by removing the unnecessary part. Repeat the process until the desired precision is reached. Return the optimal point and function value. MATLAB Implementation Below is a sample MATLAB code to implement the Golden Section Search algorithm: function [x_opt, f_opt] = golden_section_search(f, a, b, tol) phi = (1 + sqrt(5)) / 2; c = b - (b - a) / phi; d = a + (b - a) / phi; while abs(b - a) > tol if f(c) < f xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed> This code defines a function golden_section_search that finds the optimal point within the interval [a, b] using Golden Section Search. Advantages Efficient for unimodal functions. Simple to implement with minimal function evaluations. Converges faster than other search methods for specific cases.

利用MATLAB代码对OFDM的同步算法进行仿真，采用短训练序列的互相关运算进行帧同步，并利用长训练序列的互相关实现符号同步。

矩阵分解的推荐算法MATLAB实现，直接运行main.m

令人沮丧的是，Matlab没有强大的 nipals 函数，所以我为我的项目写了一个并在这里分享。这个函数主要基于R chemometrics 包中的 nipals 函数。

PSO优化算法的MATLAB语言实现，包含英文论文和代码。