Explore MATLAB source code curated for "基本思想" with clean implementations, documentation, and examples.
This resource covers the core principles and applications of Monte Carlo methods, featuring numerous MATLAB implementation examples with detailed algorithm explanations. It serves as comprehensive learning material for understanding both theoretical foundations and practical coding techniques, requiring no extraction password.
MATLAB-based analysis of Computational Fluid Dynamics fundamental principles with practical CFD case implementations including numerical methods and simulation techniques
Core principles of Computational Fluid Dynamics (CFD) with MATLAB-based simulation examples including numerical implementation approaches
When avoiding the use of correlation matrices associated with estimating input signal vectors to accelerate LMS algorithm convergence, variable step-size methods can shorten the adaptive convergence process. A primary approach is the Normalized LMS (NLMS) algorithm. The variable step-size update formula can be expressed as W(n+1) = w(n) + e(n)x(n) = w(n) + [step_size], where [step_size] = e(n)x(n) represents the adjustment term for iterative filter weight vector updates. To achieve rapid convergence, appropriate selection of the variable step-size is essential. One potential strategy involves minimizing the instantaneous squared error as much as possible, using it as a simplified estimate of the Mean Squared Error (MSE), which constitutes the foundational principle of the LMS algorithm.
Monte Carlo Algorithm Simulation Source Code - Demonstrates the fundamental principles of Monte Carlo simulations with practical implementation examples and algorithm explanations
The core idea of SVM method involves defining an optimal linear hyperplane and formulating the algorithm for finding this hyperplane as a convex optimization problem. Based on Mercer's kernel theorem, SVM employs a nonlinear mapping φ to transform the sample space into a high-dimensional (even infinite-dimensional) feature space (Hilbert space), where linear learning machines can effectively address highly nonlinear classification and regression problems from the original sample space. The SVM implementation typically involves coding support vector machine algorithms with key functions for kernel transformations and optimization solvers.
The core principle of the fast median filtering algorithm introduced in this paper is that during the sliding window movement process on the original data sequence, the current window only needs to remove its earliest element and incorporate the new element following the window to form the content of the next window. This implementation corresponds to pre-packaged algorithm code in MATLAB, designed for optimized computational efficiency.