Parallelization method of BP neural network optimized by genetic algorithm based on spark

A BP neural network and network technology, applied in the direction of genetic models, can solve problems such as inability to train, take a long time, and slow convergence speed of BP neural network algorithm, so as to achieve the effect of improving efficiency and speed of convergence

Inactive Publication Date: 2015-08-26
中电科数字科技(集团)有限公司 +1
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Problems solved by technology

[0004] The BP neural network algorithm has the shortcomings of slow convergence speed and easy to fall into local minimum points. Based on the BP neural network optimized by the genetic algorithm, the weight and threshold of the BP network are first optimized by the genetic algorithm to improve the convergence rate of the network and overcome the tendency to fall into the local minimum. local minimum deficiency
[0005] The traditional BP neu

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  • Parallelization method of BP neural network optimized by genetic algorithm based on spark
  • Parallelization method of BP neural network optimized by genetic algorithm based on spark
  • Parallelization method of BP neural network optimized by genetic algorithm based on spark

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[0030] The following and accompanying appendices illustrating the principles of the invention Figure 1 A detailed description of one or more embodiments of the invention is provided together. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details.

[0031] As mentioned above, the BP neural network parallelization method optimized by a spark-based genetic algorithm provided by the present invention can better overcome the problems under the condition of massive training data, and ca...

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Abstract

The invention provides a parallelization method of a BP neural network optimized by genetic algorithm based on spark. The method comprises the following steps: performing global evolution optimizing on a weight value of the BP neural network through the adoption of a spark parallelization programming model improved genetic algorithm, and obtaining an optimized neural network initial weight value after performing a certain number of evolution iterations, and iterating through the adoption of the parallelized BP neural network algorithm, finally outputting a network structure. In the training process, the multi-node parallel processing can be performed in each stage, the convergence speed of the BP neural network is greatly promoted, and the training efficiency is improved.

Description

technical field [0001] The invention relates to the field of machine learning algorithm parallelization, in particular to a BP neural network parallelization method optimized by a genetic algorithm based on a spark distributed computing framework. Background technique [0002] BP (Back Propagation) neural network was proposed by a team of scientists headed by Rinehart and McClelland in 1986. It is a multi-layer feed-forward network trained by the error back propagation algorithm. The main idea of ​​BP neural network, including forward propagation signal and back propagation error. In the forward propagation process, the input signal is passed to the output layer after being processed by the hidden layer. If the output value is not equal to the expected value and is greater than the acceptable range of error, enter the error backpropagation process. The error is transmitted to the input layer through the hidden layer for error adjustment. By continuously adjusting the weig...

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Application Information

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IPC IPC(8): G06N3/12
Inventor 童晓渝赵华叶定松罗光春田玲刘贵松
Owner 中电科数字科技(集团)有限公司
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