Data Prediction Using RLS Algorithm and MATLAB Implementation

The Recursive Least Squares (RLS) algorithm, originally proposed by the renowned mathematician Gauss in 1795, represents a classical data processing methodology. Gauss established that when inferring unknown parameters from observed data, the most probable values are those that minimize the sum of squared differences between actual observations and calculated values, weighted by their precision measures - this forms the foundation of the famous least squares method. Widely applied in adaptive signal filtering analysis, the RLS algorithm offers rapid convergence and insensitivity to eigenvalue dispersion in autocorrelation matrices. However, it demands substantial computational resources. This chapter focuses on RLS-based data prediction techniques and their practical MATLAB implementation, including key algorithmic components and code optimization strategies.

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Neural Network Algorithm Enhanced with Genetic Algorithm Optimization

A neural network algorithm improved through genetic algorithm optimization, widely applicable for data prediction and analytical tasks with enhanced pattern recognition capabilities

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Data Prediction Using Elman Neural Networks - Research on Power Load Forecasting Models

Data Prediction Using Elman Neural Networks - Research on Power Load Forecasting Models with Implementation Insights

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SVR (Support Vector Regression) - Machine Learning Algorithm for Predictive Modeling

SVR (Support Vector Regression) is a supervised learning algorithm based on support vector machines, primarily used for regression tasks and data prediction. It employs kernel functions to handle non-linear relationships and demonstrates robustness against outliers through epsilon-insensitive loss functions.

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Elman Neural Network for Data Prediction - Research on Power Load Forecasting Model

The Elman neural network was proposed by Elman in 1990. This model adds a context layer to the hidden layer of the feedforward network, serving as a one-step delay operator to achieve memory functionality, enabling the system to adapt to time-varying characteristics and directly reflect the properties of dynamic process systems. The Elman recurrent neural network typically consists of four layers: input layer, hidden layer (intermediate layer), context layer, and output layer. As shown in the diagram, the connections between the input layer, hidden layer, and output layer resemble those of a feedforward network. The input layer units primarily handle signal transmission, while the output layer units perform linear weighting. The hidden layer units can use linear or nonlinear activation functions, and the context layer (also called the state layer) memorizes the previous output values of the hidden layer units and feeds them back.

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MATLAB Implementation of SVM Regression Analysis

SVM Regression Analysis with Model Building and Data Prediction - Includes Code Implementation Details

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PSO-Optimized RBF Neural Network Implementation

Custom-developed PSO-optimized RBF neural network program with guaranteed functionality. This implementation features parameter optimization through particle swarm algorithms for improved prediction accuracy. Suitable for data forecasting and pattern recognition applications with customizable adaptation options.

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GRNN-Based Data Prediction: Freight Volume Forecasting Using Generalized Regression Neural Network

Freight Volume Forecasting with Generalized Regression Neural Network (GRNN) - A neural network-based data prediction model for freight volume analysis

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Data Prediction Using Generalized Regression Neural Network

Data prediction based on Generalized Regression Neural Network (GRNN), featuring cross-validation GRNN neural network prediction program with comparative analysis between BP and GRNN networks. Both networks are trained using identical datasets, demonstrating implementation approaches for neural network optimization and performance evaluation.

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Freight Volume Prediction Using GRNN

Freight Volume Forecasting Based on Generalized Regression Neural Network

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