BP Neural Network with Error Feedback Input Research and Application

To meet the growing energy management needs of the rail transit system, this study explores the configuration and scheduling strategies of rail transit energy storage. An innovative prediction-allocation-dispatch co-optimization framework based on the deep fusion of the Beetle Antennae search algorithm and back-propagation neural network is proposed. This framework solves the problem of the co-optimization of renewable energy output uncertainty and the economy of energy storage. The study adopts a Beetle Antennae-optimized backpropagation neural network prediction algorithm to improve the prediction accuracy of wind and solar power generation. A hybrid energy storage configuration system is designed by comprehensively considering the energy storage life and cost, and a multi-time scale energy scheduling optimization scheme is proposed. The experiment showed that the optimization prediction algorithm could significantly improve prediction accuracy and had superior iterative optimization effects. Compared with traditional prediction algorithms, this optimization algorithm reduced the mean square error of photovoltaic and wind power prediction by 41.11% and 34.02% respectively and reduced the time consumption by 29.95% and 33.21% compared to other algorithms. Meanwhile, the hybrid model had significant advantages in energy storage costs and net benefits, with a total scheduling cost reduction of 17.95% compared to other energy storage systems and a response speed improvement of 13.98%. This indicates that the proposed optimization algorithm and configuration model can enhance the energy utilization function and scheduling flexibility of the rail transit system, supporting the sustainable development of rail transit.

Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution for the problem. The method combines rating data and other types of user feedback data, such as reviews and image, to improve performance of the traditional recommendation algorithms. Some researchers have proposed fusion recommendation algorithms based on BP (Back Propagation) neural network and achieved better results. However, some existing fusion recommendation algorithms based on BP neural network still have some shortcomings. They rely on the assistance of the traditional recommendation algorithms. Moreover, the high complexity of the fusion processes of these algorithms possibly has negative impacts on the fusion effects. In this paper, we modify the fusion recommendation algorithm and propose the NNFR (neural networks fusion recommendation) model. This model improves the structure of BP neural network by specially designing the structure of network layers. User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. The fusion features of user reviews and ratings are directly applied to perform recommendation, in order to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results indicate that the outstanding performance of NNFR model than comparative recommendation algorithms on rating predictions and top-k recommendations. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.

Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

The rapid development of artificial intelligence technology makes Robotics more intelligent and flexible. In this context, the kinematics inverse algorithm of the Robotics has become the basis and key technology for the further development of the Robotics. The key point of the inverse algorithm of the Robotics is to coordinate the Robotics operation arm with the corresponding action actuator at the end to realize the space attitude control of the Robotics system, and to make a theoretical basis for the motion analysis of the later Robotics. However, the traditional form of Robotics kinematics inverse algorithm avoids a lot of iterative computational solution process, which increases the complexity of the whole algorithm. Therefore, based on the above situation, this paper proposes a Robotics inverse solution algorithm based on improved BP (back propagation) neural network. In this paper, in the application of the actual algorithm, aiming at the convergence problem of the traditional BP neural network algorithm, an improved BP neural network algorithm based on the excitation function is proposed. By selecting the adaptive processing function in each layer of the neural network, the selection is matched with it. The learning rate, thus improving the accuracy of the entire motion inverse algorithm. At the same time, in order to further reduce the calculation of joint quantification, this paper also creatively introduces the algorithm of plane division auxiliary dynamic model construction. The simulation results show that the inverse kinematics algorithm based on improved BP neural network proposed in this paper has obvious advantages in solving the kinematics inverse problem of six-degree-of-freedom Robotics compared with the traditional inverse solution algorithm.

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

In view of the traditional intelligent vehicle lane decision algorithm is lack of flexibility, and slow convergence speed of traditional back propagation neural network algorithm, the training time is long, easy to fall into local minimum values and without guiding network structure theory, by studying the traditional improving methods of back propagation neural network algorithm, introducing auxiliary weights adjustment parameters and contraction coefficient, abate sawtooth phenomenon, speed up the convergence speed and reduce the training time, and to some extent, improve the accuracy of intelligent vehicle lane decision for active avoidance. Through the synthetic judging three different lanes static target decision, compare the improved back propagation algorithm with the traditional algorithm in the actual lane decision algorithm for active avoidance accuracy and convergence time.

At present, photovoltaic power generation is becoming more and more important in the field of new energy power generation. The traditional BP neural network algorithm is prone to fall into local extreme points in the training process of the short-term photovoltaic power prediction model, and the training model If the global optimum is not reached, the optimization is stopped, and the weights of the initial network are randomly selected, which leads to problems such as low model prediction accuracy and low iteration efficiency. This paper proposes a short-term photovoltaic power prediction algorithm based on improved BP neural network. The method uses the Levenberg-Marquardt algorithm to replace the traditional gradient descent method for training, which improves the iterative speed of the traditional BP neural network training, and uses the genetic algorithm to optimize the initial weight of the network model, which further improves the model prediction accuracy. The simulation results show that the prediction accuracy of the improved algorithm for short-term photovoltaic power is significantly better than that of the algorithm before the improvement, and the stability and iterative efficiency are greatly improved.

In recent years, BP (Back Propagation) neural network is widely used in predictive modeling in various fields. But the BP neural network technology which used for university catering service is very few. The article is applied to the data set which is published by the EMC competition of Shanghai Jiao Tong University in 2015. We use BP neural network to analyze and forecast the university restaurant sales, and then through comparing the model with the time series forecasting method. The elements used in the model include the cycle factor, the Baidu index of the network take away, the weather information. The forecasting factors include three aspects of the 11 variables, which is also an innovation of this paper. Finally, we proved that the model we built has a good prediction result and it also has practical availability. This article also explained how the variables impact on university catering service.

To alleviate the pressure of data size, data transmission and data processing in the huge data dimension of the Internet of things., data classification is realized based on back propagation (BP) neural network algorithm. The working principle is deduced in detail. For the shortcomings of slow convergence and easy to fall into the local minimum, the combination of variable learning and momentum factors is used to improve the traditional back propagation algorithm. The results show that the optimized algorithm improves the convergence speed of the network to a certain extent. Therefore, it is concluded that the back propagation neural network has higher classification success rate when classifying multidimensional data in Internet of things.

When using traditional knowledge retrieval algorithms to analyze whether the feature input of words in multi-modal natural language library is symmetrical, the symmetry of words cannot be analyzed, resulting in inaccurate analysis results. A feature input symmetric algorithm of multi-modal natural language library based on BP (back propagation) neural network is proposed in this paper. A Chinese abstract generation method based on multi-modal neural network is used to extract Chinese abstracts from images in multi-modal natural language library. The Word Sense Disambiguation (WSD) Model is constructed by the BP neural network. After the word or text disambiguation is performed on the Chinese abstract in the multi-modal natural language library, the feature input symmetry problem in the multi-modal natural language library is analyzed according to the sentence similarity. The experimental results show that the proposed algorithm can effectively analyze the eigenvalue symmetry problem of the multi-modal natural language library. The maximum error rate of the analysis algorithm is 7%, the growth rate of the analysis speed is up to 50%, and the average analysis time is 540.56 s. It has the advantages of small error and high efficiency.

Forecasting stock indices with back propagation neural network

In order to improve the accuracy of port container throughput prediction, an improved ABC-BP prediction model based on Artificial Bee Colony (ABC) was proposed. With the advantage of global search ability and difficulty in local optimization, the weights and thresholds of Back Propagation (BP) neural network are optimized, and the optimal weights and thresholds in the network are finally determined. Referring to the existing research results, Qingdao 2014-2019 (24 quarters) GDP, port throughput, total foreign trade import and export volume were selected as network input, and Qingdao port 2014-2019 port container throughput statistics were used as network output, a total of 24 sets of data were constructed, so as to build a BP neural network prediction model. The first 20 groups of data were used as the training set, and the last 4 groups of data were used as the test set for instance verification. The results show that, compared with the traditional BP neural network and the BP neural network prediction model optimized by Genetic Algorithm (GA), the ABC-BP model can significantly improve the prediction accuracy of port container throughput.