Adaptive Automatic Encoder for Edge Computing Data Reduction for Intelligent Customized Manufacturing System

Edge computing is an important field of artificial intelligence and customized manufacturing systems, which can reduce the processing pressure of the system. An autoencoder is a classic data dimensionality reduction tool in the field of edge computing, and the number of hidden layer neurons in the autoencoder is very important. However, there is currently no effective method to determine this number. To overcome this problem, this article elaborated an adaptive automatic encoder for edge computing data reduction for intelligent customized manufacturing system from the perspective of edge computing model reduction in customized manufacturing system. Specifically, an adaptive method for determining the number of hidden layer neurons was proposed. This method can automatically adjust the number of hidden layer neurons during the training process of the automatic encoder. First, a method was designed to evaluate the importance of hidden layer neurons. Second, based on this method, the factors that affect the output results were identified. Third, based on the preset maximum and minimum thresholds, if the influence of a certain factor is less than the minimum threshold, it is considered that the corresponding neuron has little impact on the final result and the neuron is discarded. If the influence of a factor is greater than the maximum threshold, the neuron has a significant impact on the output result, and ultimately the neuron divides. The experimental results validate the effectiveness of this algorithm in intelligent cognition.