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论文类型：期刊论文

发表时间：2022-06-29

发表刊物：系统工程理论与实践

卷号：38

期号：5

页面范围：1331-1342

ISSN号：1000-6788

摘要：Men��s health has been seriously damaged due to prostate cancer in recent years. Fuzzy neural network can be used to diagnose prostate cancer, and fuzzy rules can be extracted from the diagnosis model. In order to solve the problem with low interpretable rules extracted by fuzzy neural network, a structure adaptive fuzzy neural network (SAFNN) method is proposed. By modifying the loss function, this method can control the combination of similar membership functions, adjust the structure of fuzzy neural networks adaptively and reduce the number of fuzzy rules in the process of model training. Moreover, this method can extract interpretable rules and guarantee the diagnosis accuracy. To simplify the calculation process and improve training efficiency, particle swarm optimization (PSO) algorithm is adopted to train the structure and parameters of the model. We also conduct experiment studies with the inspection data of prostate diseases provided by National Clinical Medicine Information Center. The experiment result veries the efficiency of the proposed method in prostate cancer diagnosis and interpretable rules extraction. ? 2018, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

备注：新增回溯数据