{'fraction_non_alphanumeric': 0.015706806282722512, 'fraction_numerical': 0.0034904013961605585, 'mean_word_length': 4.797979797979798, '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}}
Neural Network Modeling and an Extended DMC Algorithm to Control Nonlinear Systems

In this paper we propose a solution to the model predictive control problem for the case when the model is given by a nonlinear neural network. The solution follows the algorithm proposed by Peterson et al.[11] where the linear DMC is extended to handle nonlinear systems by updating the linear model with a `disturbance due to nonlinearities' term. Simulation results of a reaction in a CSTR are included. Results show the improvement in control of the proposed algorithm over linear DMC.