Delay Prediction Method of RBF Based on Improved Sparrow Search Algorithm
The randomness and instability of the network induced delay sequence make it difficult for a single prediction al-gorithm to predict the network delay accurately.To solve this problem,a radial basis neural network(RBF)delay prediction method based on the improved sparrow search algorithm(ISSA)was proposed to predict the network delay accurately.Firstly,to deal with the problem of uneven initial distribution of sparrow population,an improved Tent chaotic map was added to improve the distribution quality of the early population,and a new inertial weight factor was introduced to improve the discoverer location up-date strategy and expand the early optimization range of sparrow.Then the sine-cosine optimization algorithm(SCA)was intro-duced to update the follower position to avoid the population falling into local optimal.Secondly,considering the uncertainty of RBF hidden layer node center,amplitude and output layer weight,ISSA algorithm was proposed to optimize and obtain.Finally,an improved delay prediction model was built,and the predicted delay value was obtained by inputting the measured delay data.The experimental results show that compared with the traditional SSA-RBF model,the proposed ISA-RBF delay prediction model im-proves the MSE,RMSE and MAE by 69.46%,32.83%and 34.43%respectively,which can effectively predict the network delay and provide a basis for the later delay compensation.
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