Genetic Algorithm and BP Neural Network Control for Inverted Pendulum

This document presents an inverted pendulum control program based on genetic algorithm (GA) and backpropagation (BP) neural network. The system employs genetic algorithm to optimize the weights and thresholds of the neural network, resulting in superior control performance. The GA automatically searches for optimal control parameters through operations like selection, crossover, and mutation, significantly improving control accuracy and stability. The implementation involves coding chromosome representations of neural network parameters and fitness functions evaluating pendulum stabilization. The author provides detailed explanations of the program's underlying principles and implementation steps, including neural network architecture design and GA parameter configuration. The program demonstrates practical applications for controlling inverted pendulum motion through iterative training and real-time adjustment mechanisms. This research holds significant importance in robotics control domains, offering robust support for future robotic control technologies development.