Optimization of Extreme Learning Machine Algorithm Based on MATLAB and Its Application in Forecasting
Extreme Learning Machine (ELM) is a machine learning algorithm based on a single-layer feed-forward neural network, which builds the network by randomly initializing the weights and biases of the hidden layer, and uses an analytical solution or a pseudo-inverse to calculate The weight of the output layer has the characteristics of fast training speed and strong generalization ability. However, the traditional ELM algorithm has certain limitations when dealing with problems with complex nonlinear relationships. In order to further improve the performance of ELM, the pathfinder algorithm can be used for optimization, so that ELM can achieve better results in prediction problems.
Algorithm background and principle:
Pathfinder Algorithm (Pathfinder Algorithm) is an optimization algorithm based on genetic algorithm, which simulates the process of explorers searching for the best path in unknown terrain. The pathfinder algorithm combines the global search ability and local search ability of the genetic algorithm, and can effectively find the global optimal solution of the problem.
The basic principle of the extreme learning machine is to initialize the weights and biases of the hidden layer randomly, and to calculate the weight of the output layer through the analytical solution or pseudo-inverse while keeping the weights from the input layer to the hidden layer unchanged. However, when the traditional ELM randomly initializes the weights and biases of the hidden layer, it may cause the network to converge slowly or fall into a local optimal solution. Therefore, we can use the pathfinder algorithm to optimize the ELM to better discover the global optimal solution.
MATLAB source code implementation:
% 构建极限学习机算法优化架构
% 步骤1：加载数据集（例如，训练集和测试集）
load(