Network flow prediction method for optimizing extreme learning machine by improving cuckoo search algorithm

Abstract

The invention discloses a network flow prediction method for optimizing an extreme learning machine through an improved cuckoo search algorithm and relates to the technical field of intelligent computing. According to the network flow prediction method for optimizing an extreme learning machine through an improved cuckoo search algorithm smaller prediction error and shorter prediction time can berealized by adopting real number coding to represent each parasitic nest and adopting a snap-drive cuckoo search algorithm to perform parameter optimization on the extreme learning machine. Accordingto the method, the operations of allocating solutions to bird eggs, rejecting non-search, selecting worst parasitic nests through probability search, globally searching, updating Pm, updating Pa and the like by executing an snap-drive cuckoo search algorithm are performed; the algorithm is high in optimization capacity, low in calculation complexity, high in calculation speed and convergence speed, capable of conducting global search and capable of jumping out of a local optimal solution can be known.

Description

technical field [0001] The invention relates to the technical field of intelligent computing, in particular to a network traffic prediction method for improving a cuckoo search algorithm and optimizing an extreme learning machine. Background technique [0002] With the introduction of concepts such as the Internet of Things and ubiquitous networks, the network traffic data between nodes in the next-generation Internet backbone network and between nodes in the local area network will show a substantial increase, and Internet traffic will soon enter the era of big data. In the context of big data traffic, the sharp increase of network business categories has led to changes in the nature of network traffic. The traditional traffic model is no longer suitable for the analysis and prediction of Internet traffic in today's and even the next generation. Therefore, research on intelligent network traffic forecasting is imperative. Row. ELM (Extreme Learning Machine, extreme learnin...

AI Technical Summary

Problems solved by technology

[0003] However, in practical applications, since some of the network weights are randomly initialized, the model prediction results obtained after each initialization are different.

As a result, the prediction time of the extreme learning machine network is longer, and its prediction stability and accuracy cannot be guaranteed

Images