Chaotic Neural Network Model based on Simulated Annealing Algorithm

This is a chaotic neural network model based on the simulated annealing algorithm that integrates chaos theory with neural network technology to simulate and analyze complex system behaviors. The model employs simulated annealing optimization to fine-tune network weights and biases through a temperature-controlled random search process, significantly enhancing learning efficiency and prediction accuracy. By incorporating chaos theory principles through nonlinear activation functions and sensitive dependence on initial conditions, the model effectively handles nonlinear and non-deterministic problems, providing more realistic representations of real-world complexities. Key implementation aspects include adaptive cooling schedules for the annealing process and chaos-based neuron activation functions that enable rich dynamic behaviors. The model finds practical applications across multiple domains including financial forecasting, medical diagnosis systems, and engineering optimization, where it supports prediction tasks, parameter optimization, and decision-making processes. In summary, this simulated annealing-enhanced chaotic neural network model offers broad application prospects and significant research value for complex system modeling.