Abstract
Neural network is easy to fall into the minimum and overfitting in the application. The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are selected by the K-means clustering algorithm. In order to solve the problem that K-value cannot be selected automatically in the K-means clustering algorithm when conducting the selection of individuals, the K-value optimization algorithm based on distance cost function is put forward to find the optimal K-values. In addition, for the integrated output problems, the paper proposes a dynamic weight model which is based on fuzzy neural network with accordance to the ideas of dynamic weight. The experimental results show that the integrated approach can achieve better prediction accuracy compared to the traditional single model and neural network ensemble model.
Highlights
With the continuous development of artificial intelligence technology, neural network, as an important method in the machine learning field, has advantages which make it a new and favorable choice in many fields
This paper proposes the method to solve the problem of the integrated output of neural network ensemble through the establishment of dynamic weighting model based on fuzzy neural network
After the selection of the network individuals by genetic algorithms, the training sample is established according to the fitting error of the selected network individuals and the generalized regression neural network is trained to predict the future time in order to calculate the weight of network individuals during different periods
Summary
With the continuous development of artificial intelligence technology, neural network, as an important method in the machine learning field, has advantages which make it a new and favorable choice in many fields. Because of its convenience and ideal effect, this method won wide approval from experts This method was referred to as the first of the four current most important machine learning research directions [3] by the international authoritative TG Dietterich. The research on neural network ensemble is mainly concentrated on the following two aspects: individual network generation and integrated output. The research on how to obtain greater individual difference in general can be divided into three categories: data transformation, changing of the network characteristics, and individual neural network optimization. The research on the software reliability prediction based on neural network ensemble concentrates on two aspects: the individual neural network optimization. This paper proposes the method to solve the problem of the integrated output of neural network ensemble through the establishment of dynamic weighting model based on fuzzy neural network
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