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In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth. [ABSTRACT FROM AUTHOR]

With the progress of society, the quality requirements of international business enterprises for international business talents have been improved accordingly. So, it is urgent to conduct in-depth research on the teaching model and the improvement of students' practical ability. Taking international business as an example, this paper analyzes the contradiction between the supply and demand of international business technical talents by literature research. Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. By studying how to implement the professional training curriculum system construction and enterprise to talented person ability training requirements cohesion, this paper in view of the secondary vocational school of international business in today's society professional training curriculum system was modified and perfected. The ultimate purpose of this paper is to meet the demand of international business for characteristic talents and constantly promote the high-quality development of international business education. [ABSTRACT FROM AUTHOR]

Track and field is a competitive sport with a long history, and it is one of the earliest sports that started scientific training. It is also the main source of many classic theories and methods of modern competitive training, and most of them are still in use today. The purpose of this paper is to study how to analyze and discuss the content system of physical fitness training for track and field athletes and some evaluation criteria of indicators based on artificial neural network. It also describes the BP neural network. This paper puts forward the problem of training content system and index evaluation standard, which is based on artificial neural network. Therefore, the concept and related algorithms are elaborated, and the case design and analysis are carried out. The experimental results show that the physical training method is mainly based on the transformation training method. The goal is to improve the athlete's training motivation, interest, and adaptability. The training method is relatively simple in design. According to the distribution of muscles, there are more core and trunk training methods, accounting for 23.64% of physical training. [ABSTRACT FROM AUTHOR]

It is an important research direction of mental health discipline in the current era to evaluate and analyze college students' mental health by using deep learning methods and form visual data characteristics and analyzable discipline conclusions. Based on this, this paper carries out the research method of convolutional neural network by using the research concept of deep learning. Firstly, the paper summarizes the fast intelligent analysis model based on the convolutional neural network system algorithm, classifies and summarizes the unique characteristics of college students' mental health, and uses the convolutional neural network processing model to analyze, evaluate, and observe college students' mental health combined with the big data theory. Secondly, through the expansion and utilization of multi-layer neuron self-coding neural network, the psychological health of college students is evaluated and analyzed in the psychological discipline, the discrete data structure is established by using the relevant psychological data, the psychological behavior of college students is analyzed, summarized, and classified, and the data model is filled to judge the mental health status of college students. Finally, through the design of confirmatory experiments, the results show that the college students' mental health evaluation and analysis model based on deep learning is more efficient in individual data analysis. Compared with the mode of analyzing college students' mental health through in-depth learning, the traditional psychological research method has a large workload and is not suitable for the universality and consistency of college students. This paper solves this problem and provides a reference for relevant research. [ABSTRACT FROM AUTHOR]

If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human-like lane-changing model of an autonomous vehicle. The lane-changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC-based driver model with the steering wheel angle of human drivers, we found that the MPC-based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human-like lane-changing driving model based on Bi-LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi-LSTM-based human-like lane-changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane-changing conditions. [ABSTRACT FROM AUTHOR]

The important role of teaching evaluation system is embodied in: starting from the teaching goal and the vocational education teaching activities. This paper studies the optimization algorithm and optimization system, it not only makes the algorithm involve basic mathematical operations, and the computer support required for the data processing process is not high, but it also improves the evaluation of the degree of optimization. In view of these characteristics, this paper has conducted in-depth research to fully prove the feasibility and superiority of the content of this article. The specific summary is as follows: (1) Introduced the design concept of particle swarm optimization teaching evaluation system. (2) The use of object-oriented programming algorithms makes it easier for the algorithm to find an entry point, solve practical problems, and optimize the reusability of the algorithm method. (3) Particle swarm optimization based on quantum behavior, adjusting parameter values, the highest and the lowest, greatly reduces the difficulty of program parameter adjustment. (4) In terms of operation, it can quickly and efficiently complete the maintenance of teacher teaching information, evaluation relationship management of teacher teaching quality evaluation, evaluation content management, student evaluation, supervision evaluation, college leadership evaluation, evaluation performance management, and other operations. The interface is extremely humane. It adopts a web-style tour method. There are many types of functions, and the system includes common functions required for general teacher teaching information management and quality evaluation, and while providing various functions, it closely integrates the various actual needs of the college. The security performance is good. The system provides user name and password verification, which improves the security of the system. Database management is convenient and fast, providing a database-friendly management interface, and timely and accurate query. (5) This system adopts an object-oriented development method. During the development process, full consideration of the user's needs enabled the system to have powerful functions and streamlined procedures. In the end, this application software basically completed the goals required by the requirements analysis. [ABSTRACT FROM AUTHOR]

Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the increasing need of countries to produce electricity and facing the lack of resources, this research focuses on forecasting photovoltaic technology. Accordingly, in this paper, for technological research in the field of solar energy, the patents extracted from one of the most famous renewable energy databases in the United States (US patent database) between 200 and 2020 were examined. Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. The results show that in the future, photovoltaic technology research will move towards the third generation of technology (organic materials) as well as focus on environmental parameters and their effects on the performance of photovoltaic systems. [ABSTRACT FROM AUTHOR]

Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision. [ABSTRACT FROM AUTHOR]

This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called "Quantile based neural network and model integration combination algorithm." The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the "Option strategy of volatility prediction with dynamic thresholds." With the new algorithm above, the strategy improves the return-risk ratio in test set (measured by Sharpe ratio: 1.99–2.07). [ABSTRACT FROM AUTHOR]

Based on machine learning algorithms, this paper designs a crossborder e-commerce logistics service system recommendation algorithm. First, we introduce the meaning of query recommendation, analyze the mechanism of e-commerce platform shopping search, redesign the query recommendation process on this basis, establish a Markov decision process model for the problem, and solve the optimal recommendation strategy through deep machine learning algorithms. Second, we design a simple calculation example, use Python programming through a simulated shopping environment, give the solution process of the optimal recommendation strategy in the whole process, and prove the feasibility of the algorithm. The sentiment synthesis word vector is used as the input data structure of the text, the convolutional neural network model and the recurrent neural network model in machine learning are independently designed and constructed, and a shunt is proposed. The rule (shunt) realizes the operation of judging the data and inputting the two machine learning networks. The shunt fully realizes the combination of the advantages of the local feature characterization of the convolutional neural network and the timing characteristics of the recurrent neural network and achieves a more efficient and accurate electrical system. Finally, through simulation experiments, a series of data processing work such as data outlier cleaning, sliding window construction features of data variables, and training set and test set division are designed to convert regression prediction problems into classification problems to predict commodity demand. At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model. [ABSTRACT FROM AUTHOR]

This paper investigates the passivity of multiple weighted coupled memristive neural networks (MWCMNNs) based on the feedback control. Firstly, a kind of memristor-based coupled neural network model with multiple weights is presented for the first time. Furthermore, a novel passivity criterion for MWCMNNs is established by constructing an appropriate Lyapunov functional and developing a suitable feedback controller. In addition, with the assistance of some inequality techniques, sufficient conditions for ensuring the input strict passivity and output strict passivity of MWCMNNs are derived. Finally, the validity of the theoretical results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments. [ABSTRACT FROM AUTHOR]