Search

The continuous expansion of the international market makes more people pay attention to learning different languages. As one of the most popular languages, German is favored by scholars. The use of intelligent translation systems to learn German has become the main way of learning. The increasing development of science and technology is accompanied by the emergence of a large number of new German words. The traditional German intelligent translation system has a slow update speed of words and cannot recognize new words, which seriously affects the accuracy of the translation. According to these questions, in this paper, the data mining algorithm is introduced into the German machine intelligent translation system. By evaluating the German machine intelligent translation system using a data mining algorithm in four aspects: translation speed, accuracy rate, recall rate, and user satisfaction, the evaluation results found that the translation speed of the system increased by 4.68%, the accuracy rate increased by 4.2, the recall rate has increased by 3.8%, and the user satisfaction score has increased by 0.825 points. The data mining algorithm can improve the German machine intelligent translation system and users' interest in learning and promote German education. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [ABSTRACT FROM AUTHOR]