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RBF neural network optimization algorithm based on improved sparrow search algorithm

A neural network and search algorithm technology, applied in the field of neural network optimization, can solve problems such as insufficient search, strong aggregation and unable to jump out of local optimum, etc., to enhance global search ability, enrich population diversity, improve accuracy and convergence speed Effect

Pending Publication Date: 2021-02-05
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

This update method of jumping to the optimal position will make the sparrow unable to fully search for other possible optimal solutions in the process of moving to the current optimal value, and finally the aggregation is too strong to jump out of the local optimum

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  • RBF neural network optimization algorithm based on improved sparrow search algorithm
  • RBF neural network optimization algorithm based on improved sparrow search algorithm
  • RBF neural network optimization algorithm based on improved sparrow search algorithm

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Embodiment Construction

[0048] In order to deepen the understanding of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments, which are only used to explain the present invention and do not limit the protection scope of the present invention.

[0049] The embodiment discloses a RBF neural network optimization method based on the improved sparrow search algorithm, and uses RBF to train the sea clutter prediction model for illustration. figure 1 The specific steps of this embodiment are given:

[0050] Step 1: Build the RBF neural network, and determine the structure of the RBF neural network as n-h-m. The input and output of RBF are determined by the reconstructed sea clutter data, and the number of hidden layers is usually determined by the number of clusters obtained by the clustering algorithm, but the reconstructed sea clutter data to be processed is high-dimensional data, if The clustering algorithm ca...

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Abstract

The invention discloses an RBF neural network optimization algorithm based on an improved sparrow search algorithm. RBF initial parameters are optimized through the improved sparrow search algorithm,so that the sea clutter prediction precision is further improved, and the purpose of suppression is achieved. An elite reverse learning strategy is introduced, a current optimal solution is selected as an elite individual, and a reverse solution of the elite individual is generated, so that the global search capability of the algorithm is enhanced. Self-adaptive Gaussian variation is adopted to perform variation on an optimal solution and perform greedy selection, and in addition, a position updating mode for sparrow investigation early warning is also improved. And the population is promotedto evolve towards the optimal solution direction, so that the problem that sparrows are easy to fall into local optimum in the convergence process of low fitness in the sparrow search algorithm is avoided to a certain extent. The ability of the improved sparrow search algorithm to jump out of the local optimum is enhanced, and the convergence speed and precision of the RBF network optimized by theimproved sparrow search algorithm are further improved.

Description

technical field [0001] The invention belongs to the technical field of neural network optimization, in particular to an RBF neural network optimization method based on an improved sparrow search algorithm. Background technique [0002] HF Surface Wave Radar (HF Surface Wave Radar), as an emerging sea surface detection radar, is widely used in the detection and tracking of moving targets on the sea surface because of its advantages of long detection distance, all-weather, real-time and accurate detection. However, when the high-frequency ground wave radar detects the sea surface target, the sea clutter is doped in the radar echo, which constitutes the main interference to the target detection. Therefore, reducing the interference of sea clutter on detection targets is an important prerequisite for sea radar target detection. [0003] In the initial research, sea clutter was assumed to be a stationary random process, and a stochastic process model with statistical distributio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06N3/04G06N3/00
CPCG06N3/08G06N3/006G06N3/045
Inventor 尚尚杨童王召斌何康宁刘明
Owner JIANGSU UNIV OF SCI & TECH
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