Neural Network TCM Syndrome Diagnosis System Based on Adaptive Resonance Theory

Abstract

The invention discloses a neural network TCM syndrome diagnosis system based on self-adaptive resonance theory, comprising: a TCM four-diagnosis information preprocessing module, a syndrome differentiation module, a rule storage module and a visualization module. The four modules interact and support each other. The information preprocessing module of the four diagnoses of traditional Chinese medicine is connected with the syndrome judgment module, and the input vector of the processing system is used to guide the dynamic establishment of the syndrome judgment module to obtain the syndrome differentiation rules; the rule storage module is connected with the information of the four diagnosis of traditional Chinese medicine. The preprocessing module and the syndrome differentiation module establish a two-way connection relationship, providing empirical rules for the two, and the latter two read and modify the empirical rules; the visualization module is connected with the information preprocessing module Visualize diagnostic information and diagnostic rules. The invention can quickly perform incremental matching learning on new case samples, and apply the improved dialectical model SWART2 to improve the correct rate and adaptability of the system, and introduce visualization tools to improve the humanization and interactivity of the system.

Description

technical field [0001] The invention belongs to the technical field of informationized intelligent diagnosis of traditional Chinese medicine. It specifically relates to a TCM syndrome diagnosis system, which is a TCM syndrome diagnosis system based on an adaptive resonance theory neural network. Background technique [0002] Starting from the perspective of holistic view, TCM obtains the status information of the patient in a specific time and space through the four methods of diagnosis, conducts a series of speculative processes of analysis, induction, and judgment, and then identifies syndromes. In the process of traditional Chinese medicine syndrome differentiation, the accuracy of the diagnosis results depends on the doctor's knowledge and clinical experience, which has strong subjectivity and uncertainty, which greatly limits the development of Chinese medicine. In order to realize the objectification, quantification, and intelligence of TCM syndrome differentiation, o...

AI Technical Summary

Problems solved by technology

[0004] However, the neural network diagnostic models currently used by Wang Cunran and others, such as BP and counter-propagation network models, usually adopt a pre-designed network structure, and the learning algorithm is prone to fall into a local optimum, and the convergence speed is slow. The knowledge obtained from training is implicit. It is difficult to be understood, and does not support online real-time learning of incremental cases, so it cannot truly and effectively reflect the real-time dialectical relationship between syndromes and symptoms

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