RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

To solve the general model for S-parameter of microstrip line quickly, this paper proposes Particle Swarm Optimization-Neural Network (PSO-NN) algorithm, which is based on the research of Particle Swarm Optimization (PSO) algorithm and neural network algorithm. By testing and analyzing PSO-NN, PSO and BP neural network algorithm respectively with the performance check function, we find PSO-NN the best performance. Finally, PSO-NN algorithm is applied to the general model for S-parameter of microstrip line which has made use of CST software to get the training data and validation data of the S-parameter of microstrip line. By training and validating PSO-NN, PSO and BP neural network algorithm, we prove that PSO-NN algorithm has the minimum average error and standard deviation in acceptable time. Compared with CST software, the PSO-NN algorithm has shorter simulation time at the same precision level .Therefore, this paper is of great value to the research of PCB board.

A hybrid method which combines time series model and artificial intelligence method is proposed in this paper to improve the prediction accuracy of building thermal load. Firstly, a simple auto regressive (AR) model is utilized to predict present load using previous loads, the order and the parameters of AR model are identified by the data produced by DeST. Then, a 3-layer back-propagation neural network optimized by particle swarm optimization (PSO) neural network (PNN) is set up to predict the error which is derived by comparing the precious AR predicting load. The error and its corresponding meteorological data generate the training sample data. At last, the hybrid model, named autoregressive and particle swarm neural network (APNN), is obtained. It uses historical load information and real-time meteorological data as input to predict a refined real-time load by adding error to preparative load. To evaluate the prediction accuracy, this hybrid model APNN is compared with several common methods via different statistical indicators, the result show the APNN hybrid method has higher accuracy in thermal load forecasting.

The degradation and failure of the urban water supply network may lead to serious safety hazards, including pipe breaks, water supply interruptions, water resource losses, and contaminant intrusions. The risk evaluation of water supply pipeline failure in a distribution network is a challenging task, because most of the available data cannot fully reflect pipeline failure events and many of the mechanisms still cannot be fully understood. Therefore, a predictive model is urgently needed to assess pipeline failure risk based on available data. In this paper, based on the traditional risk assessment theory, seven main factors affecting pipeline failure are selected and scored, and then a pipeline failure model is established by using the particle swarm optimization (PSO) neural network. The model uses the neural network training of historical data to evaluate the failure of the water supply pipeline, and the PSO is used to optimize the neural network to effectively improve the training time and accuracy. The model error and correlation coefficient are 0.003 and 0.987, respectively. The proposed model can be used as a powerful support tool to assist infrastructure managers and pipeline maintainers in their plans and decision-making.

The Chinese word segmentation based on the improved Particle Swarm Optimization (PSO) neural networks is discussed in this paper. Firstly, a solution is obtained by searching globally using FPSO (Fuzzy cluster Particle Swarm Optimization) algorithm, which has strong parallel searching ability, encoding real number, and optimizing the training weights, thresholds, and structure of neural networks. Then based on the optimization results obtained from FPSO algorithm, the optimization solution is continuously searched by the following BP algorithm, which has strong local searching ability, until it is discovered finally. Simulation results show that the method proposed in this paper greatly increases both the efficiency and the accuracy of Chinese word segmentation.

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm's error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP's stable scheduling with a welcoming environment and transparent information. It also enhances the system's data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP.

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students' entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students' entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees.

This paper presents a technique for classifying types of wine using Neural Network Back Propagation (NNBP). In this research also developed Neural Network technique with Particle Swarm Optimization (PSO) algorithm to calculate neural weight and bias. In the experimental results, the use of PSO-based NNBP or Neural Network Particle Swarm Optimization (NNPSO) yielded better results than the classic NNBP method. This clearly shows that the classification of types of wine with NNPSO can classify all types of wine. Meanwhile, NNBP techniques can only classify one type of wine. With the Mean Square Error (MSE) limit of 0.2, the accuracy obtained with NNPSO is about 75% - 90% and NNBP around 25 - 45%.

Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Since this classical training technique has many drawbacks like stuck in the local minima, maximum number of iterations required, in this paper the training of the NN has been implemented with the opposition based with particle swarm optimization neural network (OPSONN) algorithm. These algorithms that are used for the NN training can be applied for the solutions of data classification problems. It is renowned that different techniques comparison is also as vital as by proposing a new technique for data classification. In this paper, a detailed comparative performance analysis for the training of neural network is observed on the different data sets taken from UCI repository. Results demonstrates that opposition based particle swarm optimization neural network (OPSONN) may offer efficient and best substitute to traditional training approach of the neural network for the solution of problems of data classification. The results are compared with OPSONN learning algorithm for feed forward neural network (FNN). The subsequent exactness of FNNs trained with PSO (PSONN), back propagation algorithm (BPA), and back propagation algorithm with momentum is likewise examined. The trial results demonstrate that OPSONN outperforms PSONN, back propagation algorithm (BPA), and back propagation algorithm with momentum for preparing FFNNs as far as accuracy rate and better precision. It is likewise demonstrated that an FFNN prepared with OPSONN technique has preferable exactness over one trained with different methods.

Toward a Hybrid Approach of Primitive Cognitive Network Process and Particle Swarm Optimization Neural Network for Forecasting

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

For the congestion phenomena of networks, it has been provided with a new prediction method for service flow (based on Particle Swarm Optimisation and Wavelet Neural Network Prediction PSOWNNP). Firstly, this method is using the wavelet exchange to resolve the service flow, and using its wavelet coefficient and metric coefficient as the sample data. Secondly, training the sample data is using the neural network method of the particle swarm optimisation in which it is applying the wavelet model for construction, and the prediction data for service flow will be obtained from this. At the same time, the prediction methods of wavelet neural network and BP neural network for particle swarm optimisation are analysed and compared through the simulation experiment, and the result for indicating the performance of AWNNP method is relatively good, with a tolerance of 17.21%.

Based on the meteorological data provided by Xi'an Bureau of Meteorology, this paper establishes three methods to forecast the air visibility based on the temperature, the humidity, the $PM_{2.5}$ concentration and the $PM_{10}$ concentration. The three methods include genetic neural network (GNN), particle swarm optimization neural network (PSONN) and support vector machine (SVM). All of these methods take the temperature, the humidity, the $PM_{2.5}$ concentration and the $PM_{1\mathrm{C}}$ concentration as the input variables, and the visibility as the output variable. According to the visibility prediction results of the three methods, the correlation coefficients between the predicted and the actual values of GNN and PSONN are both higher and the prediction errors are smaller than that of SVM; as a whole, the PSONN has the smallest prediction error, the highest correlation coefficient and the best predictive performance.

In order to more accurate for nonlinear function extreme, this paper used improved particle swarm optimization neural network combining with genetic algorithm method to solve the problem. In view of the particle swarm optimization algorithm is easy to appear “premature” faults, introducing the adaptive threshold, initializing particles if they were under the constraint conditions, making particles jump out to the optimal value of the position in previous search. Through the experiment, contrasts to the genetic neural network algorithm and traditional BP neural network, this method is faster in convergence and has the smallest prediction error. Finally, combining with genetic algorithm, calculating the extreme value of nonlinear function by using the above three kinds of neural network trained forecast as an individual output fitness value. The adaptive particle swarm optimization neural network proves the most close to the theoretical calculation. It shows that the method is effective.

The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental functions and involving time-consuming calculations. Particle Swarm Optimization (PSO), which is based on the behaviour of insect swarms and exploits the solution space by taking into account the experience of the single particle as well as that of the entire swarm, is similar to the genetic algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. In this paper, PSO is firstly proposed to optimize feed-forward neural network for manipulator inverse kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional GA genetic algorithm based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the simulation results verify the particle swarm optimization neural network (PSONN) is effective for manipulator inverse kinematics control.