Pulse classification and recognition optimization based on fuzzy neural networks

The human system is a complex time varying and nonlinear system, of which pulses are the output. In order to objectively and quantitatively identify and study pulse signals, a method combining artificial neural networks (ANN) and fuzzy theory was applied to classify and recognize pulse signals. Fuzzy neural networks (NN) were adopted to perform weight adjustments according to fuzzy reasoning rules, which improved the reliability and accuracy of the pulse classification results. The fuzzy NN learning algorithm was applied to the fuzzy reasoning process according to the new fuzzy reasoning rules of data extension, realizing the interaction between systems and environment and updating knowledge and optimization models. The algorithm also established the necessary foundation for pulse diagnostic expert systems to adapt and optimize.