Particle Swarm Optimization Algorithm for PID Parameter Tuning

MATLAB source code implementation of Particle Swarm Optimization (PSO) algorithm for PID controller parameter tuning

PID Auto-tuning Algorithm Program

PID auto-tuning algorithm program that automatically tunes PID parameters based on optimal evaluation functions from step response curves, implementing intelligent parameter optimization through system identification and performance metrics calculation.

Model Optimization Prediction Based on Backpropagation Neural Networks with MATLAB Implementation

Backpropagation (BP) Neural Network is a supervised learning algorithm for neural networks. It is a hierarchical network structure consisting of an input layer, one or more hidden layers (middle layers), and an output layer. Each neuron in a layer is fully connected to all neurons in the adjacent layers, while there are no connections between neurons within the same layer. The network learns by comparing the actual output with the desired output (teacher signal). When a learning pattern is presented to the network, neurons generate connection weights based on the input response. The algorithm then propagates the error backwards from the output layer through the hidden layers, adjusting the connection weights to minimize the difference between the expected and actual outputs. This iterative process continues until the global error converges to a predetermined minimum value, completing the learning phase. This chapter focuses on applying BP neural networks for PID parameter tuning and digital recognition technology.