Application of Particle Swarm Optimized BP Neural Network in Boiler Combustion Optimization

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Resource Overview

Implementation of particle swarm optimization for BP neural networks in boiler combustion optimization, featuring detailed code explanations, simulation diagrams, and algorithm workflow. The program uses function-based naming convention - simply rename the main file to match the internal function name and execute.

Detailed Documentation

In boiler combustion optimization, we have successfully implemented a particle swarm optimized BP neural network application. The system employs PSO to optimize BP neural network weights and thresholds, enhancing prediction accuracy for combustion parameters. Our implementation includes comprehensive code documentation with MATLAB function descriptions like pso_optimize_bp() for the optimization core and neural_network_train() for BP network training. Simulation diagrams illustrate convergence curves and prediction vs actual value comparisons. For operational convenience, the program follows a function-based naming structure - users simply need to rename the main file to match the internal function identifier and execute directly. This implementation demonstrates significant value for improving combustion efficiency through intelligent optimization algorithms.

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