Research On Cost Prediction Of Branch Networks Decoration Engineering

With the economic globalization and the increase in capital demand,people’s emphasis on solving the contradiction between capital supply and demand and the liquidity of the securities market has continued to increase,and the role of banks as a financial intermediary has been further weakened.At the same time,the reform of interest rate marketization has increased a series of risks faced by banks,such as credit and liquidity risks.Under the environment of continuous downward pressure on my country’s economy,competition among domestic banks has intensified.Large banks have seized market resources according to their strategic plans.One of the measures is to increase or renovate their bank branch networks.The business outlets of major banks have been updated quickly and the number has increased greatly in a short period of time.The decoration investment of branch networks has become a fixed expenditure of banks.In view of the bank is not a specialized cost management institutions and the lack of professional staff,making the network of decorative cost lack of accuracy and authenticity.By accurately predicting the cost decoration projects for bank branch networks,the construction unit can compare and select the best construction plan in the investment decision-making stage,and the design unit can optimize the design plan within the investment limit.For the construction unit,the optimization bid can be achieved.The quotation strategy increases the bid winning rate,and the bank can more effectively determine and effectively control the cost of the decoration project of the bank branch networks.Recently,many intelligent algorithms have been continuously developed with the widespread application of computer technology.The selection of inappropriate prediction methods will lead to poor prediction results.For example,the BP neural network requires a large amount of samples,and the convergence speed and slowness of the gene expression method are the same.Shortcomings such as poor generalization ability,the gray system theory model is relatively simple,and the prediction results have large deviations.Therefore,this research improves the support vector machines through the particle swarm optimization,proposes a prediction method based on the particle swarm optimization support vector machine,and applies it to the cost prediction of the decoration engineering of bank outlets.First of all,based on the analysis of relevant literature,with the composition of the decoration cost of bank branch networks as the starting point,the two aspects of decoration and installation were analyzed respectively,and a reasonable and objective cost index system of bank business outlet decoration engineering was constructed.On this basis,for the selected indicators,the principal component analysis method is used for dimensionality reduction,and independent comprehensive indicators are obtained as the input set of the prediction model,which reduces the complexity of the sample and improves the operating efficiency of the model.Secondly,with the help of the advantages of particle swarm algorithm in optimizing parameters,to solve the problem of blindness in parameter setting in the support vector regression machine model,find the optimal hyperparameters,and establish an optimized parameter prediction model based on the PSO algorithm,which improves the stability and stability of the model.Accuracy.Finally,taking the bank branch decoration project in Suzhou City in the past3 years as an example,33 sets of sample data were collected and sorted out,and the SPSS software was used for data preprocessing,and then MATLAB software was used for simulation analysis.The analysis of the prediction results shows that the support vector machine model of particle swarm optimization has high accuracy,which verifies the applicability and effectiveness of the model.