# An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
## 1 Introduction
[PERSON] et al. [13] proposed to combine the effective features extracted by DBN with the sequence classifier of the recurrent neural network to improve the search performance of word hypotheses. [PERSON] [14] et al. linked DBN with neuroscience and proposed a DBN based on glial cell mechanism to improve the performance of DBN for extracting data features. Although these improved deep belief networks (IDBNs) have good effects in many fields, if DBN can build a network model independently during the training process, it may greatly improve training efficiency. [PERSON] et al. [15] determined the neural network structure by self-organizing structure to improve the learning efficiency of neural network. However, when it comes to DBN, the self-organizing structure becomes very complicated [16]. [PERSON] et al. [11] proposed a Net2 Net technology to accelerate the experimental process by transferring knowledge from the previous network to a new, deeper and wider network. Inspired by this, transferring the important knowledge learned to the newly added substructure becomes an optimization method to determine the DBN structure. [PERSON] et al. [17] pointed out that transfer learning is the transfer of knowledge from the source task to the target task. He applied transfer learning to convolutional neural networks (CNNs). They transfer the feature layer knowledge learned from one CNN (source task) to another CNN (target task) to achieve transfer learning. It also shows that the knowledge of the source task will affect the performance of the target task. In fact, the idea of transfer learning can be applied to neural networks as a new learning framework to solve model training problems [11]. In view of the above analysis, we will propose a method based on knowledge transfer to construct the DBN network structure to determine the best model structure to improve training efficiency and model performance. First, initialize a basic three-layer structure, including the input layer (visible unit layer), hidden layer, and output layer. Through pre-training, the learned weight parameters are fixed. This is the first step of pre-training. Secondly, using the technology of knowledge transfer to transfer the weight parameters of the initial structure to a new and more complex structure, according to the expanded network rules, including expanding the network width and depth, independently constructs the best DBN network structure. At this point, the pre-training phase is completed. Next, the partial least square regression (PLSR) method is used to fine-tune the parametersof the IDBN structure. [PERSON] et al. [18] proposed that the back propagation (BP) algorithm has local minimum, long training time and gradient dispersion problems. [PERSON] et al. [19] combined PLSR with the extreme learning machine, which overcomes the defects of the extreme learning machine itself, improves the prediction accuracy, and makes the model more robust. Therefore, using PLSR algorithm instead of BP algorithm can avoid the problems of BP algorithm. Finally, two types of prediction experiments are performed on the public data sets of Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k). Compared with other fixed models, IDBN has higher prediction accuracy. The structure of this paper is as follows: the first section introduces the related work on DBN, the second section introduces the improved deep belief network prediction model, including its construction algorithm, the third section gives the experimental process and the proof results, and finally the summary of this article.