RBF neural network optimization method based on improved particle swarm optimization

Abstract

The invention belongs to the technical field of neural network optimization, and particularly relates to an RBF (Radial Basis Function) neural network optimization method based on an improved particle swarm algorithm, which takes a piecewise function as a particle swarm inertia weight change strategy and takes a transformed sigmoid function as a change strategy of a particle swarm learning factor. An optimal RBF neural network initial parameter is searched through an improved particle swarm algorithm, so that a more accurate prediction model is trained to predict sea clutters. According to the method, a particle swarm optimization process is divided into three stages: the first stage is mainly used for searching a global optimal general position, the second stage is evolved from global search to local exploration, and the third stage is mainly used for local fine exploration. The three optimization stages are clear in division of labor, so that the particle swarm has relatively strong global search and local exploration capabilities, the optimization precision and convergence speed are improved, and the precision and stability of the RBF neural network are improved.

Description

technical field [0001] The invention belongs to the technical field of neural network optimization, and specifically relates to an RBF neural network optimization method based on an improved particle swarm algorithm. Background technique [0002] With the continuous upgrading of radar technology, all kinds of radars are more and more widely used in civil and military fields. When radar detects at sea, the echo is mixed with high-intensity sea clutter, which has a great impact on both the monitoring of the marine environment and the detection of sea targets. In this context, the realization of low-cost, high-precision sea clutter prediction and suppression will greatly improve the radar's ability to monitor the ocean, and play a key role in the observation of the national marine environment and the enhancement of national defense forces. [0003] Scholars have established many classic statistical models of sea clutter through in-depth research on the statistical characterist...

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Problems solved by technology

[0007] The purpose of the present invention is to solve the technical problem that the existing PSO algorithm is easy to fall into the local optimum and the late convergence speed is slow, and on this basis, it provides a RBF neural network optimization method based on the improved particle swarm optimization algorithm

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