scholarly journals Research on Prediction of Investment Fund’s Performance before and after Investment Based on Improved Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cong Gu
Keyword(s):  
Neural Network ◽  
Performance Prediction ◽  
Traditional Method ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Past Research ◽  
Fund Performance ◽  
Good Prediction ◽  
Investment Fund ◽  
Security Fund

There are more and more popular investment fund projects in the continuous economic development; the prediction and performance continuity become hot topics in the financial field. Scholars’ enthusiasm for this also reflects the domestic fund primary stage progress, and there is a huge application demand in China. The prediction of fund performance can help investors to avoid risks and improve returns and help managers to learn more unknown information from the prediction for the sake of guide market well and manage the market orderly. In the past research, the traditional way is to use the advantages of neural network to build a model to predict the continuous trend foundation performance, but the author found that the traditional single neural network (NN) algorithm has a large error value in the research. With the discussion, the particle swarm optimization (PSO) algorithm is added to the radial basis function (BRF) neural network, and PSO is conditioned to optimize and improve the RBF NN combining the advantages of both sides; a new set of PSO-RBF neural network security fund performance prediction method is summed up, which optimizes the structure and workflow of the algorithm. In the research, the author takes the real data as the reference and compares the prediction results with the traditional method RBF and the improved PSO-RBF. In the prediction results of the continuous trend, the highest value, and the lowest value in the period of the security fund performance, the new PSO-RBF has a good prediction in the fund performance prediction, and its accuracy rate is greatly improved compared with the traditional method Sheng, with good application value, and is worth popularizing.

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

2014 ◽  
Vol 953-954 ◽  
pp. 800-805 ◽  
Author(s):  
Meng Di Liang ◽  
Tie Zhou Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Charged State ◽  
Improved Genetic Algorithm ◽  
State Prediction ◽  
Radial Basis

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

scholarly journals Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

2020 ◽  
Author(s):  
Fuying Huang ◽  
Tuanfa Qin ◽  
Limei Wang ◽  
Haibin Wan
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Delay Time ◽  
Prediction Accuracy ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Prediction Methods ◽  
Embedding Dimension ◽  
Ecg Signals ◽  
Ecg Data

Abstract Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

AN Improved RBF Neural Network Model Based on Hybrid Learning Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2202-2207
Author(s):  
Zhao Hu Deng ◽  
Yan Qin Zhang
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Clustering Algorithms ◽  
Hybrid Learning ◽  
Descent Method ◽  
Learning Ability ◽  
Steepest Descent Method ◽  
Training Samples

When building the radial basis function (RBF) neural network with traditional method, the property of the network is easily influenced by the distribution of training samples. The learning ability and generalization ability are hard to achieve the optimum. In this paper, it presents a new method to solve this problem. In the method it replaced the traditional clustering algorithms with genetic algorithms to optimize the distribution of RBF. At the same time it combined the steepest descent method with GA to solve the binary defect of GA encoding. After experiments the results showed that the constructed neural network has a better architecture and more accuracy than that built with traditional method.

Error Modeling and Compensation of Linear Motors Positioning Stage Based on RBF Network

2010 ◽  
Vol 139-141 ◽  
pp. 1744-1748
Author(s):  
Jian Lin ◽  
Mu Lan Wang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Error Model ◽  
Error Modeling ◽  
Learning Ability ◽  
Random Errors ◽  
Rbf Network ◽  
Linear Motors ◽  
Positioning Stage

The error measuring, modeling and compensation techniques for the positioning stage driven by NC linear motors are studied. The error source of the positioning stage is analyzed, the positioning errors are measured by the laser interferometer, and the neural network error model is set up by RBF algorithm. In order to evaluate the accuracy of RBF network prediction method, part of the error samples are used to test. A DSP-core linear motor experimental platform is built up, the error compensation experiments are conducted, the real-time requirement is proved to be met. The simulation and experimental results indicate that the RBF neural network error model trained by samples has a good learning ability and generalization ability, the positioning accuracy is improved significantly, and the effect of random errors on the system is reduced also.

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