{'fraction_non_alphanumeric': 0.015206372194062274, 'fraction_numerical': 0.0, 'mean_word_length': 5.368663594470046, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Approximate dynamic programming of continuous annealing process

Approximate dynamic programming method is a combination of neural networks, reinforcement learning, as well as the idea of dynamic programming. It is an online control method which bases on actual data rather than a precise mathematical model of the system. This method is suitable for the optimal control of nonlinear systems, and can avoid the problem of dimension disaster. It can effectively solve the non-linearity of the plant or the uncertainty problem caused by the uncertainty of the system modeling. So, it is suitable for processing the complex system and task of time-varying. The heating section of the continuous annealing furnace consumes a large number of energy, and the dynamic programming method has some limitation for solve the problems. We design the optimization controller for the heating section of the annealing furnace based on the approximate dynamic programming method. In this paper, it mainly gives the basic structure and algorithm of the action-dependent heuristic dynamic programming method (ADHDP), and designs the temperature optimization controller of the heating section in the continuous annealing furnace based on the ADHDP method. Simulation shows the temperature controller based on ADHDP has some theoretical and practical significance for the future practical application.