发表机构
Indian Institute of Technology Indore（印度理工学院印多尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出IFW-BLS模型，通过波损失和直觉模糊分数实现残差级与样本级双鲁棒性，采用Nesterov加速梯度优化，在UCI数据集及噪声污染场景下性能优于传统BLS。
AI 中文摘要
宽学习系统（Broad Learning System）是一种高效的随机学习模型，通过特征节点和增强节点扩展网络宽度，无需深度反向传播即可估计输出权重。然而，其标准最小二乘训练存在两方面脆弱性：（i）由噪声、异常值或损坏标签导致的大残差会主导目标函数；（ii）所有样本被视为同等可靠，即使部分样本处于模糊或局部冲突区域。本文提出IFW-BLS，即具有直觉模糊波损失的宽学习系统，在一个优化模型中解决这两类脆弱性来源。第一种鲁棒性机制是残差级保护，通过用有界、平滑且非对称的波损失替代平方损失实现：有界性防止极端残差产生无界影响，非对称性允许在主导误差方向变化时对正负偏差进行不同惩罚。第二种机制是样本级可信度控制，通过直觉模糊分数实现，该分数结合了全局类中心一致性与局部邻域冲突。所得模型对经可信度加权的残差计算波损失，因此不可靠样本会被降权，随后有界损失进一步限制极端误差的影响。采用基于Nesterov加速梯度的优化器求解所提目标，避免了传统BLS中使用的显式矩阵求逆。在UCI基准数据集上的实验验证了所提IFW-BLS模型优于基线模型；额外的损坏实验也表明，在噪声和异常值污染下，其性能比BLS更稳定。
英文摘要
Broad Learning System is an efficient randomized learning model that expands network width through feature and enhancement nodes and estimates the output weights without deep backpropagation. Its standard least-squares training, however, is vulnerable in two different ways: (i) large residuals caused by noise, outliers, or corrupted labels can dominate the objective, and (ii) all samples are treated as equally reliable even when some lie in ambiguous or locally conflicting regions. This paper proposes IFW-BLS, an Intuitionistic Fuzzy Wave Broad Learning System that addresses these two sources of fragility within one optimization model. The first robustness mechanism is residual-level protection, obtained by replacing the squared loss with the bounded, smooth, and asymmetric wave loss. Boundedness prevents extreme residuals from receiving unbounded influence, while asymmetry allows positive and negative deviations to be penalized differently when the dominant error direction varies. The second mechanism is sample-level credibility control, obtained through intuitionistic fuzzy scores that combine global class-center consistency with local neighborhood conflict. The resulting model evaluates the wave loss on credibility-weighted residuals, so unreliable samples are down-weighted before the bounded loss further limits the effect of extreme errors. A Nesterov accelerated gradient based optimizer is used to solve the proposed objective, avoiding the explicit matrix inversion used in conventional BLS. Experiments on UCI benchmark datasets validate the superiority of the proposed IFW-BLS model over the baseline models; additional corruption experiments also show more stable performance than BLS under noise and outlier contamination.
CommentsAccepted at International Conference on Neural Information Processing (ICONIP), 2026
