Traditional BP Neural Network Algorithm Research Articles

Improved BP neural network algorithm to SOC estimation of mine power supply

Since Ni MH battery has the characteristics of high energy density and good stability, it is mainly used as a backup power supply for mining at present, but the accuracy of estimation the SOC of the power supply is poor. To enhance the precision of estimation and extend the lifespan of the battery, based on a single nickel-hydrogen battery, this paper applies a new BP neural network to rise the self-adaption and momentum terms. At the same time, the discharged power is included in the Input feature quantity of the estimation network. The temperature, current and terminal voltage are also included in the input characteristic quantity of the estimation network. There is output during training, and then the momentum term is adjusted based on the Variance of variance error by comparing the actual output. The error of the estimated network should be back propagated from the output to the input to correct the weight. The direction of change in the error determines the adaptive learning rate of the improved BP neural network. The speed of the algorithm is increased by 72% and the error rate is less than 5%, compared to the traditional BP neural network algorithm.

Research on Improved BP Neural Network Prediction Based on Niche Particle Swarm Optimization Algorithm

As for data prediction, a prediction model is proposed based on BP neural network. This method is based on the traditional BP neural network algorithm to improve the problems of slow convergence speed and easy to fall into local optimum. The idea of niche is introduced into the particle swarm optimization algorithm to improve the global search ability and convergence speed, find the global optimum value, and then use LMBP neural network to continue to optimize the parameters to improve the prediction effect.

Short-term Power Load Forecasting Based on Improved BP Neural Network from Genetic Algorithm and Simulated Annealing Algorithm

As one of the common methods of load forecasting, the traditional BP neural network algorithm has been favored by scholars for many years due to its strong learning ability and adaptive ability, and is often used by scholars to forecast power load. However, BP neural network also has its own shortcomings, that is, low algorithm efficiency, local minimization and other problems. In this paper, GA algorithm and SA algorithm are used to modify the weights of BP neural network, and a short-term load forecasting model, namely BP-GSA model, is established. The least mean square error prediction model is obtained through training. Then using BP-GSA model and traditional BP neural network model, using MATLAB software to predict the daily power load of a city from July 1 to July 10. The forecast results show that the load forecast curve obtained by BP-GSA model is closer to the actual load curve. That is to say, BP-GSA load forecasting error is less than the traditional BP neural network load forecasting error, which proves that BP-GSA model has better forecasting effect.

Analysis of Teaching Effect of Korean Education Course Based on Data Acquisition Technology.

Teaching evaluation is a comprehensive judgment of teachers' teaching effect and students' learning outcomes. It is an essential basis for comprehensive curriculum reform. There are many teaching evaluation systems for Korean majors, generally based on teachers' behavior discrimination and ignoring students' learning process and effect. The existing teaching evaluation system has problems such as heavy workload, slow calculation speed, and intense subjectivity. Based on the characteristics of Korean courses, this study constructs a teaching rating system for Korean courses in universities centered on language learning through data collection, correlation analysis, association rules, and other methods to optimize the student teaching evaluation index. At the same time, the machine learning algorithm is introduced into the teaching evaluation process to construct the teaching evaluation model and realize the automation of the teaching evaluation process. The weighted Bayesian incremental learning method is used to solve the cumulative problem of data acquisition samples. The experimental results show that the accuracy rate of classification using the weighted naive Bayesian algorithm to construct the model can reach 75%. Obviously, due to the traditional Bayesian algorithm and BP neural network algorithm, it is suitable for the teaching evaluation model of Korean majors. It provides a theoretical basis for the development of language education informatization.

Simulation Path of Network Microvideo Personalized Recommendation Based on Improved Ant Colony Algorithm

With the rapid development of Internet technology, network information not only brings many conveniences to the majority of users but also brings about the problem of information overload. It is increasingly difficult for users to accurately obtain the information they need from the vast ocean of information. This problem has led to the research of recommendation technology, especially the simulation path of online microvideo personalized recommendations. The video recommendation model in this study adopts the idea of an ant colony algorithm. The evaluation data and browsing record data generated by users are the basis of video recommendation, and the microvideo feature model and user preference model are abstracted from the original data by improving the ant colony algorithm. The recommendation algorithm is then used to recommend online microvideos that meet users’ preferences. There are two recommendation methods, one is to find similar students through the user preference model of each student, and find the microvideo content that the students have not learned; the other is to use the user preference model and the content feature model to match the content that conforms to the user’s preference. Compared with the traditional BP neural network algorithm, GA-BP algorithm, and PSO-BP algorithm, the mean square error MSE value of the ACO algorithm for microvideo scoring classification is reduced by 11.41%, 5.93%, and 2.41%, respectively. It can be seen that the classification of video ratings by the improved ACO algorithm has lower average errors than other algorithms. The network microvideo personalized recommendation scheme designed in this study has very practical significance.

Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network

The traditional optimization algorithm of communication resource allocation in a complex network has the disadvantage of weak antijamming ability, and the communication quality decreases obviously when the number of users is large. In China’s large urban network applications, mobile phones and other networks can have problems such as reduced network efficiency when there are more access users at some communication base stations, thus affecting user network usage. An optimization algorithm of communication resource allocation in the complex network based on an improved neural network is proposed. Increase inertia improves the traditional BP neural network algorithm, using the average path length, clustering coefficient, and connectivity distribution index analysis of the complex network; the improved Hopfield neural network is utilized to confirm each user volume size; it is concluded that their users are able to get the number of subchannels, through the instantaneous channel coarse pair gain dynamic channel allocation, calculating bit load matrix at the same time, minimize transmission power, and achieve bit loading and power allocation and communication resource allocation optimization. Experimental results show that the proposed method has better application performance by introducing the improved neural network and suppressing the external interference on the basis of enhancing the communication effect.

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

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.

BP neural network-based adaptive spatial-temporal data generation technology for predicting ceiling temperature in tunnel fire and full-scale experimental verification

This study proposes a novel adaptive spatial-temporal data generation method to achieve precise ceiling temperature perception in tunnel fires based on the modified BP neural network. In the method, an automatic update of training data set procedure is coupled in the BP neural network, which establishes an effective coarse-to-fine data generation method from coarse monitored data to fine ceiling temperature prediction in spatial-temporal scale. The method belongs to a kind of data-driven algorithms for ceiling temperature prediction in tunnel fires, which is not limited to the special fire scene. Additionally, full-scale fire experiment was conducted to verify the effectiveness of the method in China's largest tunnel fire experimental platform. Twenty-three thermocouples measured the ceiling temperature of the tunnel fire experiment. In contrast to the experimental results and the results obtained from the traditional BP neural network, the method can be used an effective numerical tool to predict the precise fine ceiling temperature spatial-temporal distribution, and the prediction precision is higher than the traditional BP neural network algorithm.

Research on Piano Informatization Teaching Strategy Based on Deep Learning

Applying information technology to piano teaching can effectively avoid these problems. The application of information teaching means can not only enrich teaching methods but also enhance students’ interest in learning. Deep learning (DL) focuses on immersing students in knowledge and learning situations, emphasizing critical thinking, and realizing the intrinsic value of knowledge. Understanding DL theory is of great significance to deepening the teaching reform in China. BP algorithm is a very effective algorithm for prediction and evaluation. In this paper, the complex factors affecting the quality of piano information teaching are comprehensively considered, and the improved BP neural network (BPNN) algorithm is used to evaluate and predict the quality of piano information teaching. The model structure of BPNN for evaluating and predicting the quality of piano information teaching is given and simulated in MATLAB. The results show that the evaluation and prediction method overcomes the subjective factors of expert evaluation and obtains reasonable results. Compared with the traditional BPNN algorithm, it has good applicability.

Local Media Image Propagation Algorithm and Its Governance in the Age of Artificial Intelligence.

With a large number of images provided by TV and other media flowing into the Internet and the reduction of technical barriers, images have not only become a daily practice for people to record their lives and communicate their behaviors but also become an important means for the public to express their discourse in the cyberspace. Therefore, it is of great significance to analyze the image propagation algorithm using artificial intelligence. This paper mainly studies the algorithm analysis and governance of local media image propagation in the era of artificial intelligence. In this paper, the media is the research object, with its daily dissemination of video works as the research text, in order to discover the ethical problems in its dissemination activities as the purpose, integrating disciplinary knowledge to analyze the ethical problems in this art form, and trying to find out the fundamental measures to solve the problem. The advantages and disadvantages of the video recommendation intelligent algorithm based on the BP neural network are analyzed. By comparing different algorithms, it can be seen that the video recommendation accuracy of the BP neural network algorithm based on swarm optimization (FEBP) is 15.8% higher than that of the traditional BP neural network algorithm. These intelligent algorithms are added into the image transmission system, in order to achieve the goal of improving the image transmission and recommendation effect.

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

This article aims to explore the intelligent fuzzy optimization algorithm for data mining based on BP neural network. Although the database technology has been improved with the increase of the amount of data, facing the explosive growth of the amount of data, the previous database management methods have been unable to meet and analyze the hidden knowledge in this scale of data. Therefore, it is important to find better automated data processing methods to satisfy the classification and analysis of massive data. However, the current BP neural network is not yet perfect. This method has some problems, such as slow convergence speed. The problem is reflected in the problem of pattern recognition and insufficient generalization ability and stability. Based on the above description, the research content of this paper is an intelligent fuzzy optimization algorithm for data mining based on BP neural network. Considering that the training of the BP algorithm is based on the weight correction principle of error gradient descent, the genetic algorithm is good at global search, but it does not have accurate local searchability. Therefore, this paper uses the weight of the genetic algorithm. This paper improves BP neural network based on a genetic algorithm. The experimental simulation results of Iris show that the quantity of hidden nodes usually increases with the number of training samples. ACBP algorithm can construct a better network structure based on the number of training samples. And through the experimental comparison of the traditional BP neural network algorithm, it is concluded that the improved algorithm can allow data mining technology to mine relatively more ideal data from complex environments.

Perception Analysis and Early Warning of Home-Based Care Health Information Based on the Internet of Things

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things (IoT) technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real-time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three-axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.

Optimization of a Wireless Sensor Network‐Based Smart Elderly Location Management System

In this paper, wireless sensors are used to design and build a location management system for smart aging, and the optimization of this system is analysed. The function and structure of the positioning system are designed and implemented. Next, the system commissioning process and field implementation results are summarized and analysed to guarantee the reliability and integrity of the system. Finally, the system requirements and implementation are integrated to realize the basic requirements of the IoT‐based elderly positioning management system and reserve the interface for later expansion. Finally, the system excellence and functional usability tested through unit testing as well as comprehensive testing. In this paper, a theoretical analysis of the prediction algorithm is carried out for the disease prediction module in the health monitoring software of the smart senior care system. To improve the accuracy of the prediction, the traditional BP neural network algorithm is optimized using DS evidence theory, thus fusing multiple sets of prediction results obtained from the BP neural network into a more accurate set of data, and the performance of the algorithm before and after the improvement is compared. The IoT‐based home health management for the elderly starts from the health service demand of the elderly, explains the basic concept of IoT technology and home health management for the elderly, and analyses the feasibility of home health management for the elderly and the advantages of IoT technology in‐home health management for the elderly; through the field research, the IoT‐based home health management platform for the elderly is carried out from three aspects of users, business, and technology. The design covers the platform architecture and functional modules of the IoT‐based senior home health management platform, which can solve the problem that the elderly can spend a comfortable life in their old age without going home.

Nuclear Magnetic Image Segmentation of Rectal Cancer Based on Improved Genetic Neural Network

Image segmentation is the basis of image analysis and understanding, and has an unshakable position in the field of computer vision. In order to improve the accuracy of nuclear magnetic image segmentation of rectal cancer, this paper proposes an improved genetic neural network algorithm for the problems of traditional BP neural network algorithm. In order to enhance the network performance, this paper improves the genetic neural network from the two aspects of fitness function and genetic operator, which makes the training speed and convergence precision greatly improved. Target samples were analyzed by image histogram analysis, and the improved genetic neural network was used to learn the samples to obtain the training network. Taking the pixel matrix of the image as the input vector, it is put into the trained network for classification, and finally the segmentation is realized. The simulation experiment proves that compared with the classical image segmentation method, the improved genetic neural network image segmentation method has a good segmentation effect and is a feasible image segmentation method.

Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network

With the rapid development of neuroinformatics and related intelligent algorithms, the research of recognition and classification based on EEG signals is becoming more and more important and valuable. With the progress of science and technology, the related research of EEG signal recognition and processing has been gradually applied to rehabilitation medicine, intelligent information processing, and other cross-cutting fields. As one of the most important research directions in the field of the brain-computer interface, motor imagery EEG has a wide range of applications. At the same time, it shows a good application effect in the process of application practice. At present, the main EEG recognition and analysis algorithms always have some problems and defects in data processing, such as low signal-to-noise ratio, unclean noise filtering, and high data dimension. In this paper, based on the improved BP neural network algorithm, weight splitting technology is added to the traditional BP neural network algorithm. In order to solve the filtering problem, this paper uses the non-linear mapping function of the traditional BP neural network, and intelligently trains the small weight particles by combining the particle swarm filter algorithm, so as to improve the filtering performance of the whole BP algorithm. Based on the above two algorithms, the problem of low signal-to-noise ratio (SNR) and unclean filtering in EEG data processing caused by fast weight degradation in traditional BP algorithm can be solved. Finally, according to the actual data of brain-computer interface, this paper compares the improved BP neural network algorithm with the traditional BP neural network algorithm in recognition and analysis of motor imagery EEG signals. The experiment shows that the proposed algorithm has obvious advantages in recognition accuracy and analysis effect.

An Efficient Optimization Algorithm for Extreme Value of Nonlinear Function Based on the SAGA and BP Algorithm

A common way to solve the problem of how to obtain the optimal experimental conditions and the experimental results based on limited experimental data is that use of the traditional genetic algorithm optimization BP neural network algorithm (BPGA). However, the experimental results are often unstable and the error of the optimal results is also relatively large. Therefore, we propose a method of sinusoidal adaptive genetic algorithm (SAGA) double optimization BP neural network. The simulation results show that our proposed algorithm effectively improves the accuracy of the extreme value optimization of the nonlinear function. The network prediction error reaches -0.008 to 0.008, the average extreme value is accurate to 0.0084, and the optimal extreme value is accurate to 0.00007.

Near infrared spectroscopy detection of the content of wheat based on improved deep belief network

In order to solve the complicated problem of traditional detection of the content of Wheat, a method for predicting the content of wheat components by near infrared spectroscopy based on improved deep belief network is proposed. In this paper, wavelet transform is used to preprocess near infrared spectroscopy of wheat, and then a quantitative analysis model of wheat’s moisture, protein and ash content is established by using deep belief network. And combined with the random hidden algorithm, the network model is sparse processed, and the sparse network is obtained. So as to improve the accuracy and stability of the network. The experimental results show that using the improved deep of belief network to establish the quantitative analysis model on the content of wheat, and the correlation coefficient of moisture, protein and ash content were 0.9978, 0.9928, 0.9920, standard error of prediction were 0.0069, 0.0628,0.0535. Compared with the traditional deep belief network (DBN) and the traditional shallow learning BP neural network algorithm, the prediction results have been significantly improved.

Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm

In view of the improvement measures of the optimization control algorithm for the ignition system of the hydrogen-fueled engine, the L-M neural network algorithm, Powell neural network algorithm and the traditional BP neural network algorithm are used to optimize the ignition system. The results showed that L-M algorithm not only can accurately predict the hydrogen-fueled engine ignition timing, but also has high precision, high convergence speed, a simple model and other outstanding advantages in the training process, which can greatly reduce the workload of human engine bench tests. Only a small amount of engine bench test is carried out, and the obtained sample data can be used to predict the ignition timing under the whole working conditions. The mean square error of the optimization results based on L-M algorithm arrives at 0.0028 after 100 times of calculation, the maximum value of absolute error arrives at 0.2454, and the minimum value of absolute error arrives at 0.00426.

Hydrophobic Detection Study of Composite Insulator Based on BP Neural Network Grading

With the rapid development of industry and agriculture in our country, the atmospheric contamination degree is intensifying, which has become the main factors of influencing the electrical equipment insulation level. In order to ensure the security of system operation, the periodic detection of the hydrophobic of composite insulator in the operation is re- quired to determine its safety. According to the existing problem of low detection accuracy of the present composite insu- lator hydrophobic detection, this paper proposes a BP neural network grading based composite insulator hydrophobic de- tection model. The model is optimized and improved on the basis of the traditional BP neural network algorithm and car- ries out positive and negative transmission after input samples, then compares tonsure weight and the size of the error function, calculates according to the comparison results, adjusts the weight of nodes and results in error reduction of the traditional BP neural network algorithm. Experimental simulation results show that BP neural network grading based composite insulator hydrophobic detection model is superior to transitional BP neural network algorithm on convergence and network error, which can be well applied into the composite insulator hydrophobic detection.

Research of Orange Quality Classification Technology Based on BP Neural Network Algorithm

BP algorithm is a classical neural network algorithm. We analyzed the deficiency of traditional BP neural network algorithm, designed new S function and momentum method strategy, optimized the algorithm parameters. We use the new algorithm in the classification of orange images, take color and shape features as input value, the experimental results proved that our algorithm is faster and the classification accuracy rate reaches to 90%