Comparative Analysis of Genetic Neural Networks in Control Systems
To evaluate the comparative performance of genetic neural networks in control systems, we conducted simulation experiments using indoor temperature control as a case study. With temperature targets set at 18°C and 20°C while maintaining consistent parameters, we compared standard neural networks against genetic algorithm-optimized neural networks. The implementation involves using MATLAB's Neural Network Toolbox for baseline models and custom genetic algorithm code for optimization. Simulation results demonstrate that genetic algorithm-optimized neural networks exhibit superior generalization capability and faster convergence rates through population-based weight optimization and fitness-driven selection processes.