Method of detecting echo state network weak signal in chaotic background and based on improved teaching-learning-based optimization(ITLBO) algorithm

A technology for echo state network and teaching optimization, which is applied in biological neural network models, computer simulations, etc., and can solve problems such as difficulty in selecting parameters for echo state network models

Inactive Publication Date: 2017-09-08
NANJING UNIV OF INFORMATION SCI & TECH
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[0004] In order to solve the deficiencies in the prior art, the object of the present invention is to provide a method for detecting the weak signal of the echo state network based on the improved teaching optimization algorithm in a chaotic background, and the method utilizes th...

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  • Method of detecting echo state network weak signal in chaotic background and based on improved teaching-learning-based optimization(ITLBO) algorithm
  • Method of detecting echo state network weak signal in chaotic background and based on improved teaching-learning-based optimization(ITLBO) algorithm

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[0024] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0025] The method for detecting the weak signal of the echo state network based on the improved teaching optimization algorithm in the chaotic background comprises the following steps:

[0026] Step 1: Determine the number of optimization parameters, first determine the value of the reserve pool size N and the sparseness SD, and set the initialization parameters of the echo state network; set the number of teaching times g=0, and the maximum number of teaching times is G; set the initialization parameters of the echo state network Including: the spectral radius SR of the internal connection matrix, the input unit scale IS, the input unit displacement ISH, the teacher signal scale TS, and the parameter range of the teacher signal displacement TSH.

[0027] Step 2: Perform real number encoding within the set parameter range to generate M po...

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Abstract

The invention discloses a method for detecting the weak signal of the echo state network based on the improved teaching optimization algorithm in the chaotic background. The method adopts the improved teaching optimization algorithm (ITLBO) algorithm to optimize the parameters of the echo state network model. Determine the number of optimized parameters, the value of the reserve pool size N and the sparsity SD, and encode the rest of the parameters; secondly, find the optimal echo state network model parameter combination through the teaching stage, learning stage, and feedback stage of ITLBO and compare these Modeling, training and prediction of parameters, analysis of the single-step prediction error and judging whether there is a weak target signal in the chaotic background noise, using this method to simulate the Lorenz chaotic background and the actual sea clutter signal, accurately and quickly Detect weak signals; overcome the shortcomings of difficulty in selecting parameters of the echo state network model, and improve work efficiency.

Description

technical field [0001] A detection method, especially a method for detecting weak signals in an echo state network. Background technique [0002] Chaos phenomenon is a kind of irregular motion produced by nonlinear deterministic system, which widely exists in many fields such as meteorology, hydrology, communication and economy. Chaos has the characteristics of internal randomness, overall stability and local instability, short-term predictability and long-term unpredictability. In recent years, with the continuous deepening of chaos theory research and its wide application in signal processing, automatic control, power and financial short-term forecasting and other fields, the modeling and forecasting of chaotic time series has become a very important research in the field of chaos. direction. [0003] With the emergence of artificial intelligence methods, more and more researchers apply them to time series forecasting. Among many artificial intelligence methods, neural ...

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Application Information

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IPC IPC(8): G06N3/10
CPCG06N3/10
Inventor 行鸿彦沈洁
Owner NANJING UNIV OF INFORMATION SCI & TECH
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