Deep belief network parameter optimization method based on artificial bee colony and deep belief network parameter optimization system thereof

Abstract

The invention discloses a deep belief network parameter optimization method based on an artificial bee colony and a deep belief network parameter optimization system thereof. The method comprises thesteps that a deep belief network model is constructed; and the learning rate of the deep belief network model acts as the problem parameter, the energy function of the deep belief network model acts as the objective function, iterative optimization is performed on the learning rate of the deep belief network model by using the artificial bee colony algorithm to find the optimal learning rate of the deep belief network model and the network parameter under the condition of the minimum energy value, and the optimal learning rate of the deep belief network model and the network parameter under the condition of the minimum energy value act as the initialization parameters of the deep belief network. Deep belief network parameter optimization is performed by using the artificial bee colony algorithm so that the speed of convergence of the learning rate and the network accuracy can be enhanced; and the optimal learning rate and the network parameter under the condition of the minimum energyvalue act as the initialization parameters of the deep belief network so that the fitting degree can be enhanced. The deep belief network parameter optimization method based on the artificial bee colony and the deep belief network parameter optimization system thereof can be widely applied to the field of data mining.

Description

technical field [0001] The invention relates to the field of data mining, in particular to an artificial bee colony-based deep belief network parameter optimization method and system. Background technique [0002] With the rapid development of the "Internet +" era and the rapid growth of data, the information society has entered the era of big data. Due to the high-dimensional and complex characteristics of big data, mining useful information from data requires the guidance of machine learning algorithms. In machine learning algorithms, deep learning simulates the mechanism of the human brain to interpret data, and can automatically form high-level features by combining low-level features. Complex functions can be expressed in multiple steps to reduce complexity. In the deep learning algorithm, the deep belief network (DBN) is a network model with multiple hidden layers. It uses the mechanism of the computer to simulate the human brain to process data. It only needs fewer p...

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Problems solved by technology

[0004] At present, there are no related reports on the application of the artificial bee colony algorithm to optimize the parameters of the deep belief network.

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