Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform

With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students’ online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of Online Education and Teaching Evaluation System Based on GA‐BP Neural Network

Feng-hu

2021

“…Up to now, a lot of data have been produced in the comprehensive performance analysis and bearing capacity test of the bridge prestress under fatigue load [4]. How to extract, collect, and store the mechanical properties of the bridge through these redundant data has become the main research direction of the research on the mechanical property change and ultimate load effect of the bridge prestress under the fatigue load [5]. Scholars conducted fatigue and postfatigue static load tests on 11 partially prestressed concrete test beams.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

Wang

2021

During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.

“…LSTM neural network is long short memory neural network, and CRF neural network is conditional random field algorithm. According to the combination of the features of the two algorithms, the classification can be accurately distinguished according to the semantic and grammatical relationship between the upper and lower texts [ 18 ]. Finally, the purpose of data analysis and detection is achieved.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] English Grammar Detection Based on LSTM‐CRF Machine Learning Model

Mao

2021

Deep learning and neural network have been widely used in the field of speech, vocabulary, text, pictures, and other information processing fields, which has achieved excellent research results. Neural network algorithm and prediction model were used in this paper for the study and exploration of English grammar. Aiming at the application requirements of English grammar accuracy and standardization, we proposed a machine learning model based on LSTM-CRF to detect and analyze English grammar. This paper briefly summarized the development trend of deep learning and neural network algorithm and designed the structure pattern of radial basis function neural network in grammar semantic detection and analysis on the basis of deep learning artificial neural network theory. Based on the morphological features of English grammar, a grammar database was established according to the rules of English word segmentation. In this paper, we proposed an improved conditional random field CRF (Conditional Random Field) network model based on LSTM (Long Short-Term Memory) neural network. It can improve the problem that the traditional machine learning model relies on feature point selection in English grammar detection. The machine learning model based on LSTM-CRF was used to recognize English grammar text entities. The results show that the English grammar detection system based on the LSTM-CRF model can simplify the process structure in the recognition process, reduce the unnecessary operation cycle, and improve the overall detection accuracy.