Neural network prediction algorithms in tool production

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.

There are innumerable objects found on the Web. Both the popular objects that are requested frequently by the users and unpopular objects that are rarely requested exist. Hot spots are produced whenever huge numbers of objects are requested by the users. Often this situation produces excessive load on the cache server and original server, resulting in the system becoming a swamped state. Many problems arise, such as server refusals or slow operations. In this paper, a neural network prediction algorithm is suggested in order to solve the problems caused by the hot spot. The hot spot would be requested in the near future is prefetched to the proxy servers after the prediction of the hot spot; then the fast response for the users' requests and a higher efficiency for the proxy server can be achieved. The hot spots are obtained by analyzing the access logs file. A simulator is developed to validate the performance of the suggested algorithm, through which the hit rate improvement and the request among the shared proxy servers are load-balanced.

A nonlinear Hammerstein-Wiener model predictive controller based on LSSVM is built to describe the dynamic characteristic of a continuous stirred tank reactor (CSTR), which is made up by a linear optimal component and radial basis function neural networks in series, using BP neural network to train the input sequences of the predictive control, solving the nonlinear predictive control laws by the quasi-Newton algorithm, and a neural network predictive control algorithm is achieved based on LSSVM Hammerstein-Wiener model. The simulation results of CSTR show that this approach is effective tracking and controlling product concentration.KeywordsHammerstein-Wiener modelLeast squares support vector machinesBP neural networkNonlinear predictive control

This paper presents a design of an artificial neural network algorithm for prediction and management of electric loads for the optimal operation of a microgrid with sources of renewable energy. The hybrid power generation system is composed of a photovoltaic array, wind turbines, public power grid, electric loads and battery bank as a storage system. A dynamic neural network is implemented to determine the optimal amounts of energy that must be obtained from the sources, to reduce costs and improve efficiency. Simulation results demonstrate that generation of each energy source can be reached in an optimal form using the proposed design.

The increasing demand for cloud computing resources has led to a commensurate increase in the operating power consumption of the systems that comprise the cloud. In this paper, we introduce a novel framework combining load demand prediction and stochastic state transition models. We claim that our model will lead to optimal cloud resource allocation by minimizing energy consumed while maintaining required performance levels. We characterize the ability of neural network and auto-regressive linear prediction algorithms to forecast loads in cloud data center applications. In this paper, the performance of our models against two sets of data at multiple look-ahead times is also presented.

NN-adaptive predictive control for a class of discrete-time nonlinear systems with input-delay

Stock market are broadly referring to the collection of exchanges and venues where buyers and sellers meet to exchange equity shares of public corporations. Stock market are components of free-market economy because they enable democratized to access to trading and exchange of capital for investors of all kinds. In stock market, there could be lose your entire investment. If a company does poorly, investors will sell, sending the stock price plummeting. When you sell, will lose the initial investment. To overcome this, with the comparison and analyzed a variety of Neural Network prediction methods and algorithms, the pipeline works with LSTM (Long Short-Term Memory) algorithm are more efficient and help to give more accuracy of prediction. The goal of this project is to predict the Stock price moment in the various time frame and the factor that market price will show the additional accurate and efficiency.

In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

Performance analysis and prediction of asymmetric two-level priority polling system based on BP neural network

In the domain of geotechnical engineering analysis, fast Lagrangian analysis based on the finite difference method is the most commonly used numerical analysis method. It is a classical numerical algorithm applied to calculate the in situ stress. In this paper, we propose a deep learning (DL) architecture called the enhance-and-split feature capsule network embedded in fully convolutional neural networks (ES-Caps-FCN) to predict the in situ stress for a strain-softening model when using the finite difference method for the numerical computation. Experiments indicate that this novel approach is stable and convergent. Compared with some classical prediction methods including linear regression analysis and a deep neural network, the mean squared error of our proposed algorithm is as low as 0.059866%, which is lower than the 0.616676% of the deep neural network prediction algorithm and the 0.978495% of the conventional machine learning algorithm. Additionally, the calculation efficiency of fully trained deep learning models is superior to that of the conventional finite difference method. Therefore, DL is a feasible and promising fast and accurate surrogate for the finite difference method for solving the in situ stress.

Traditional data mining algorithm had limited capacity at short-term hydrological forecasting with low accuracy, and made little use of the error between the data set and the results to correct the results. Considering of the traditional hydrology predictive algorithm combined only with the external associated factors, but had not fully excavated the predictive data itself, the BP neural network predictive algorithm with error feedback input was proposed. Because the algorithm makes full use of the relationship between the forecasting result and the system information entropy, it makes the forecasted results more accurate, and achieves a satisfied result.

In this paper, the ideological and political education path is designed according to the spirit of Yan’an. In order to verify the feasibility of the path, as well as to make the student evaluation more effective and objective, the article combines the K-means algorithm and proposes a K-means algorithm based on neighborhood spatial density optimization (NDK-means). The algorithm assists in solving the problem of grading the reliability of student assessment and realizes the labeling of student assessment reliability data. Then, based on the student assessment reliability characteristics and data labeling results, a BP neural network prediction algorithm (CSA-BP) based on crow search improvement is proposed to model the student assessment reliability. The results show that the percentages of freshmen to seniors who are satisfied with the Civic Education proposed in this paper are 85.9%, 82.26%, 76.75%, and 72.45%, respectively, in terms of grade level. Students from urban, rural, and rural areas, in terms of their place of origin, all feel satisfied with the current teaching of Civics and Political Science at 80% or more.

Early transformations in odor representation

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

Intrusion detection, as a dynamic security protection technology, is able to defense the internal and external network attacks. Using Artificial Bee Colony algorithm to optimize the parameters of neural network is to avoid the neural network falling into a local optimum, can solve the problem of slow convergence speed of the neural network algorithm. Also Artificial Bee Colony algorithm can deal with the problem of finding the optimal solutions in a very short period of time. In this paper, An Artificial Bee Colony optimized neural network algorithm is applied to intrusion detection. And the experimental results shows that the optimized method has better detection accuracy and efficiency than the single BP neural network.