A PSO-BFGS neural network training algorithm

A PSO-BFGS, neural network training technology, applied in the direction of biological neural network model, calculation, calculation model, etc., to achieve the effect of running speed improvement, high portability, high convergence efficiency and global search ability

Pending Publication Date: 2019-05-28
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a kind of PSO-BFGS neural network training algorithm in order to overcome the deficiencies in the prior art, namely based on the neural network training parallel algorithm of particle swarm algorithm (PSO)

Method used

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  • A PSO-BFGS neural network training algorithm
  • A PSO-BFGS neural network training algorithm
  • A PSO-BFGS neural network training algorithm

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] like figure 1 Shown is a schematic diagram of the neural network structure, including: input layer, hidden layer and output layer. All adjacent neurons are connected together, and each connection corresponds to a weight. The number of neurons in the input layer corresponds to the input number of a set of training data, the number of neurons in the output layer corresponds to the output number of a set of training data, and the number of neurons in the hidden layer is set according to the needs, which is generally larger than that of the input layer neurons. quantity.

[0041] image 3 It is the design flowchart of the algorithm of the present invention, specifically as follows:

[0042] 1. Task division: The PSO-BFGS neural network training algorithm generally includes two types of tasks, namely computing tasks and control tasks. The control task is complet...

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Abstract

The invention discloses a PSO-BFGS neural network training algorithm. The method comprises the following steps: (1) dividing tasks; (2) utilizing the PSO algorithm for global search, using the generated optimal solution as the input of the BFGS by iteration, carrying out local fine search through the BFGS algorithm, (3) carrying out parallelism degree division on the PSO algorithm and the BFGS quasi-Newton algorithm, (4) achieving a neural network training error evaluation function through the PSO algorithm and the BFGS algorithm, (5) achieving the PSO algorithm, The algorithm is a neural network training parallel algorithm based on a particle swarm optimization (PSO) and a BFGS quasi-Newton algorithm, a GPU is adopted as a neural network training computing device, and compared with otheroptimization algorithms, the algorithm has high convergence efficiency and global search capability. OpenCL is used as a programming language for realization, and compared with the realization of using a CUDA language, the realization of using OpenCL as the programming language has higher transportability and can be used on GPUs and FPGAs of different manufacturers.

Description

technical field [0001] The invention belongs to the field of high-performance computing and machine learning, and specifically relates to a PSO-BFGS neural network training algorithm. Background technique [0002] Artificial neural network is an information processing system, it can learn any input-output relationship through a series of data, and establish an accurate model. Currently, one of the main challenges facing artificial neural networks is training. Before training, the neural network does not carry any information; after training, the weight value of the neural network can be determined, so as to establish an accurate model based on the training data. The process of determining the weight value of the neural network is an optimization process, that is, the neural network obtains a more accurate fitting weight value through repeated iterative calculations through various optimization algorithms. Traditional neural network training optimization algorithms are main...

Claims

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Application Information

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IPC IPC(8): G06N3/063G06N3/00
Inventor 李佳峻刘强
Owner TIANJIN UNIV
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