Abstract This paper proposes an extension of the type-1 and singleton fuzzy logic system for dealing with multiclass classification problems. The proposed extension enables a fuzzy classifier to generate more than one output, thereby avoiding the use of binary decomposition strategies when multiclass classification problems are considered. Additionally, with the goal of improving classifier performance, the scaled conjugate gradient training method was applied, as well as its modified version using the differential operator R · . The effectiveness of the proposed extension was evaluated using data from the UCI Machine Learning Repository based on well-established classification metrics. The numerical results reveal a significant reduction in computational complexity when using the proposed extension compared to the traditional decomposition strategy, as well as improved convergence speed when using the scaled conjugate gradient training method.

中文翻译：

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由缩放共轭梯度方法训练的用于多类分类问题的类型 1 和单例模糊逻辑系统的扩展

摘要 本文提出了用于处理多类分类问题的类型 1 和单例模糊逻辑系统的扩展。提议的扩展使模糊分类器能够生成多个输出，从而在考虑多类分类问题时避免使用二元分解策略。此外，为了提高分类器的性能，应用了缩放共轭梯度训练方法，以及使用微分算子 R · 的修改版本。基于完善的分类指标，使用来自 UCI 机器学习存储库的数据评估了拟议扩展的有效性。