Comparative Analysis of Genetic Neural Networks in Control Systems

To better understand the comparative effectiveness of genetic neural networks in control systems, we conducted a case study using indoor temperature control as an example. We established temperature control targets of 18°C and 20°C while maintaining all other parameters constant. We performed simulation experiments comparing standalone neural networks against genetic algorithm-optimized neural networks. The implementation typically involves coding neural networks with backpropagation algorithms for baseline models, while genetic algorithm optimization employs chromosome encoding of network weights, fitness evaluation using mean squared error, and evolutionary operations including selection, crossover, and mutation. Simulation results lead to the following conclusion: neural networks optimized with genetic algorithms demonstrate significant effectiveness. The genetic algorithm optimization process enhances neural networks by improving their generalization capability through global search mechanisms and accelerating convergence speed via population-based optimization techniques.