A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features. The fuzzy radial basis neurons (FRBNs) were defined and used as parameterized membership functions, and typical feature samples of each pattern subclass were used as kernel centers of the FRBN to realize the embedding of the diverse prior feature knowledge and the fuzzification of the input signals. According to the signal categories of FRBN kernel centers, nodes in the rule layer were selectively connected with nodes in the FRBN layer. A fuzzy multiplication operation was used to achieve synthesis of pattern class membership information and establishment of fuzzy inference rules. The excitation intensity of each rule was used as the input of T-S fuzzy classifier to classify the input signals. The FRBAIN can adaptively establish fuzzy set membership functions, fuzzy inference, and classification rules based on the learning of sample set, realize structural and data constraints of the model, and improve the modeling properties of imbalanced datasets. In this paper, the properties of FRBAIN were analyzed and a comprehensive learning algorithm was established. Experimental validation was performed with classification diagnoses from four complex cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that, in the case of small-scale imbalanced datasets, the proposed method significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.

中文翻译：

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模糊径向基自适应推理网络及其在时变信号分类中的应用

提出了一种模糊径向基自适应推理网络（FRBAIN），用于多通道时变信号融合分析和特征知识嵌入。将径向基核函数的先验信号特征嵌入机制与模糊系统基于规则的逻辑推理能力相结合的模型由多通道时变信号输入层、径向基模糊化层、规则层、正则化层和 TS 模糊分类器层。采用动态模糊聚类算法将样本集模式类划分为具有相似特征的若干子类。模糊径向基神经元 (FRBN) 被定义并用作参数化隶属函数，将每个模式子类的典型特征样本作为FRBN的核中心，实现多种先验特征知识的嵌入和输入信号的模糊化。根据FRBN核中心的信号类别，规则层的节点与FRBN层的节点有选择地连接。采用模糊乘法运算实现模式类成员信息的综合和模糊推理规则的建立。将每个规则的激励强度作为TS模糊分类器的输入，对输入信号进行分类。FRBAIN 可以基于样本集的学习自适应地建立模糊集隶属函数、模糊推理和分类规则，实现模型的结构和数据约束，并改进不平衡数据集的建模特性。本文分析了FRBAIN的特性，建立了一种综合学习算法。基于 12 导联心电图信号对四种复杂心血管疾病的分类诊断进行了实验验证。结果表明，在小规模不平衡数据集的情况下，与实验中的其他方法相比，所提出的方法显着提高了分类精度和泛化性。