Abstract
Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.
Highlights
IntroductionJargon such as neural networks and classification algorithms has been flooded with communication methods and entire personal lives
It can be seen that the combination of SOM method and LM method has a better effect on improving the accuracy of sample simulation
The research of neural network in the field of fault diagnosis has achieved good results, the structure and training times of neural network have a great influence on the fault diagnosis ability of neural network
Summary
Jargon such as neural networks and classification algorithms has been flooded with communication methods and entire personal lives. The basic principle of neural network is to imitate the human brain neuron network, trying to have the same intelligent learning ability and logical analysis ability as human; the human brain has billions of neurons, which cannot be simulated by current computers. This article can start from the structure of the human brain and learning principles and try to simulate a network that can think like a brain as much as possible. Combining theoretical and experimental result analysis, this paper finds that the traditional neural network algorithm is expected to break through its computing bottleneck in the future Internet of Things system and usher in rapid development
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