Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Abstract

In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks. Based on RBF, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund. RBF neural network is used to build the risk warning model of social security fund operation. Then, put forward the corresponding risk treatment scheme to the warning signal. Finally, the RBF neural network is used for comprehensive risk warning. In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal. This paper uses an improved ant colony algorithm to optimize the parameters of the RBF neural network, which overcomes the shortcomings of the traditional RBF neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the RBF neural network. It has the characteristics of good output stability and fast convergence speed. On this basis, the prediction model based on the improved ANT colony-RBF neural network is established, and the MATLAB software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers. The results show that the network has small calculation error, fast convergence, and good generalization ability.

Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory

“…This method has been proven by researchers to be useful in finding the most important input features for modeling. Studies on radial basis function neural networks [16,17] and fuzzy neural networks [54,55], for example, have discussed this issue. For deep learning models, the feasibility of this theory has also been successfully proven on LSTM [15] and RNNs [4].…”

“…RBF has previously been combined with deep learning models to extract key features for modeling for complex time series prediction [4,15]. However, RBF is not limited to time series [16,17]. Thus, the current paper applies this concept to find key input features for product fault detection.…”

Implementation of Non-state Pension Provision in Ukraine in the System of Strengthening Social Protection

“…To ensure the high-quality functioning of the social insurance fund system in countries, modern scientists use different modelling techniques that provide clear forecasting data for the further functioning of economic systems. For example, Yang (2021) presented an interpretative structure model based on the application of the RBF neural network, illustrating the construction of a risk correlation hierarchy diagram and providing a scientific method for managing risks for a social insurance fund. This made it possible to create a prediction model based on the advanced ANT colony-RBF neural network, which makes the model more accurate and provides a more reliable basis for decision-making.…”

Retracted: Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”