A Key Data Query Model for Introducing Neural Network Extreme Learning Machine
The structure of cyberspace data has high similarity,and the continuous incremental updates of massive data lead to redundancy and deviation issues in key data query results.Therefore,a method of querying key data based on neural network and extreme learning machine was put forward.Firstly,the problems of key data query were described by modeling.On this basis,the neural network and extreme learning machine were used to construct a model of key data query.Secondly,useless data and duplicate data in database were preprocessed.And then,the least square solution of weight norm was output to prevent the algorithm from being got in local optimization.Combined with the output matrix,the query model was trained.Finally,the output result was the result of key data query.In order to prove the performance advantage of the proposed method,a comparative experiment was designed.Experimental results show that the root mean square error of key data query is less than 1.2 after using the proposed method,and the maximal mean absolute percentage error is 4.1%.In addition to these data,the relationship number F can reach 0.6.And the utilization rate of network nodes is always less than 20%.The experimental data above prove that the proposed method has higher accuracy of data query and stronger applicability.