No abstract

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.

Language is the cornerstone of children’s learning knowledge and exploring the world. Language teaching is an indispensable part of early childhood education, which can help children improve their communication ability and help children communicate with classmates and teachers in school. The teaching form of preschool language education is too single, which does not conform to children’s own learning ability. Therefore, in teaching, teachers should actively optimize the language environment to make children willing to express themselves in language: introducing game activities to make children want to express themselves in language. With the help of situational display, children like to express themselves in language and carry out performance activities to give children the opportunity to express themselves in language. With the help of home force, children dare to express themselves in language. Children are in a dynamic stage of rapid growth. In order to enable children to learn language effectively, teachers should make clear the development center of children and their mastery of language. To guide language teaching in accordance with students’ aptitude for a long time, at the same time, we should update our rigid views on children at any time, boldly innovate the content and thinking mode of children’s language teaching activities, and mobilize children’s initiative and enthusiasm to participate in language learning, so that they can achieve the language learning goals of being able to speak, loving to speak and bravely speaking. This paper selects the learning situation of preschool children as the object, establishes an evaluation model by using the hybrid GA-BP neural network method, and makes an empirical analysis on the development of preschool education in Liaoning Province. The GA-BP neural network model is trained by MATLAB 7.0, and the simulation results show that the model can evaluate the development of preschool education scientifically, and it is scientific and practical. According to the results of this paper, the corresponding evaluation model of preschool education language teaching quality is put forward, which makes a good prediction preparation for children to learn language better in advance.