Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.

With the continuous development of deep learning, due to the complexity of the deep neural network structure and the limitation of training time, some scholars have proposed broad learning, the Broad Learning System (BLS). However, BLS currently only verifies that it has excellent effects on some of the network training data sets, and it does not necessarily have excellent effects on some actual data sets. In response to this, this paper uses the effect of BLS in predicting the unevenness of yarn quality in the yarn data set, and proposes a BLS-based multi-layer neural network (MNN) for the problems, which is called Broad Multilayer Neural Network (BMNN).

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Traditional hydraulic drive experiments present a number of challenges. During the hydraulic transmission experiment, the equipment is easily damaged and must be frequently updated, which makes it difficult for a large number of students to study at the same time; the traditional offline, monotonous, and boring experiments make it difficult for students to increase their interest in learning from what is inherent; and most undergraduate students have to study at home due to the impact of COVID-19. Therefore, students need an excellent teaching system that allows them to perform experiments at home and improve their learning efficiency. A teaching system for the undergraduate hydraulic transmission course was designed to meet the needs of the hydraulic transmission course and to stimulate students’ interest in learning. This teaching system allows students to spend more time outside of the class to analyze experimental results and relate concepts presented in lecture courses to experimental results. Finally, a course on hydraulic drives taught at Nanchang University was used to evaluate the effectiveness of this teaching system. The analysis based on positive student feedback and academic performance shows that the proposed teaching system is an effective learning tool for undergraduate students in their learning process.