Nonlinear time sequence prediction method based on small-world scale-free network

Abstract

The invention relates to a nonlinear time sequence prediction method based on a small-world scale-free network. The method comprises: replacing a random network based on a small-world scale-free network to obtain a small-world scale-free echo state network SSESN, clustering random distribution neurons by adopting a subtractive clustering optimization algorithm, adaptively obtaining an optimal clustering scheme, and improving the clustering performance of an SSESN storage pool network; constructing a small-world scale-free network connection model through intra-cluster connection and inter-cluster connection according to the clustering condition of neurons; extracting connection information of each node, calculating an internal connection matrix of the reserve pool, constructing a nonlinear time sequence prediction model based on the small-world scale-free network according to the generated SSESN reserve pool network and the input / output training sample set, and the prediction precision of the nonlinear time sequence prediction model is improved.

Description

technical field [0001] The invention relates to a nonlinear time series forecasting method, in particular to a nonlinear time series forecasting method based on a small-world scale-free network. Background technique [0002] Time series prediction is widely used in meteorology, industry, economy and other fields. However, most time series data in the real world show strong nonlinear characteristics. Using nonlinear prediction models to predict nonlinear time series has gradually become a research topic at home and abroad. hotspots. The commonly used echo state network prediction method (ESN), which uses reserve pool calculation, uses a large-scale sparsely connected random reserve pool to ensure the mapping ability of the model to nonlinear data, and the learning method is efficient and easy. [0003] However, the traditional ESN uses a random network as a reserve pool, and its randomness makes the model have problems such as aimless training, poor adaptability, and few adj...

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Problems solved by technology

[0003] However, the traditional ESN uses a random network as a reserve pool, and its randomness makes the model have problems such as training without purpose, poor adaptability, and few adjustable network parameters.

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