Efficiency Analysis of Pattern Recognition Based on Feedforward Neural Network
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At present, the research in the field of AI focuses on the integration of intelligent recognition methods, and a new feedforward neural network model for pattern recognition is proposed. The model has the property that the training is consistent with the classification standard in practical application, which makes the pattern recognition classification more reasonable and natural. The change of the corresponding error function can speed up the training speed of the network. Since fuzzy neural network technology has many excellent abilities such as knowledge storage and uncertain information processing, the application of neural network in pattern recognition can make up for the defects and deficiencies in the original technical field. Therefore, the application of neural network in pattern recognition has become a research focus. Feedforward neural network model is the most widely used neural network model so far, especially in the field of pattern recognition. Combined with the design process of simple character recognition system, this paper discusses the basic mathematical principle of feedforward neural network algorithm, and gives the basic flow of using this algorithm to solve the problem of pattern recognition. This paper focuses on the principle of network training, and deeply analyzes the relevant factors affecting the efficiency of network training.
Feedforward neural network, Pattern recognition, Intelligent domain