Binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method

A chaotic time series, particle swarm optimization technology, applied in the field of digital signal processing

Inactive Publication Date: 2011-10-05
SHANDONG UNIV
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But the two parameters obtained may not b

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  • Binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method
  • Binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method
  • Binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method

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[0045] A chaotic time series prediction method based on binary particle swarm optimization algorithm, such as figure 1 As shown, the steps are as follows:

[0046] 1) Particle swarm initialization: initialize the position and velocity of the particles, and set the size of the particle swarm;

[0047] Take the number of particle swarms as 30-50, and set the total number of iterations as T; using the BPSO algorithm, the algorithm can be expressed by the following formula:

[0048] v ij (t+1)=wv ij (t)+c 1 r 1j (t)[p best (t)-x ij (t)]+c 2 r 2 (t)[g best (t)-x ij (t)],

[0049] x ij (t+1)=x ij (t)+v ij (t+1). (1)

[0050] Where t is the number of iterations of the particle swarm, x ij (t) represents the position of the i-th particle in the t-th iteration, v ij (t) stands for x ij (t) the probability of taking a value of 1, j represents the dimension of the particle position, c 1 and c 2 are constants, usually take the value of 2, r 1 and r 2 is a random numb...

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Abstract

The invention discloses a binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method, and belongs to the technical field of digital signal processing. The method comprises the following steps of: after a known chaotic time series is subjected to phase space reconstruction, selecting neighboring points of a predicted state by means of Euclidean measurement, training the data of the neighboring points by using a local area linear model, solving coefficient parameters of the model, and predicting the chaotic time series by using the model, wherein the longer chaotic time series can be predicted by repeating the predicting step or changing the prediction step length. The parameters of the local area linear model and the parameters of the phase space reconstruction are of different values, the optimal values of the parameters can be sought by using a BSPSO algorithm, and the number of the selected neighboring points is greater than the embedded dimension, so that interference of false neighboring points is avoided as much as possible, and the prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of digital signal processing, in particular to a chaotic time series prediction method based on a binary particle swarm optimization algorithm. Background technique [0002] In the early days of scientific development, natural things were first understood from linear relationships, and more linear interactions between things were studied. When theorists explore many phenomena in nature, they always try to establish linear models under the premise of ignoring nonlinear factors, at least try to linearize nonlinear models, and use linear models to approximate or locally Instead of nonlinear prototypes, nonlinear effects are discussed in terms of small perturbations to linear processes. However, there are many problems in the research process that cannot be solved by linear methods. Therefore, nonlinear science has received more and more attention. The sensitivity of the initial value of the chaotic system ma...

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Application Information

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IPC IPC(8): G06N7/08G06N3/12
Inventor 江铭炎崔笑笑
Owner SHANDONG UNIV
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