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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System
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A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System

机译：元认知复值区间类型2模糊推理系统

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摘要

This paper presents a complex-valued interval type-2 neuro-fuzzy inference system (CIT2FIS) and derive its metacognitive projection-based learning (PBL) algorithm. Metacognitive CIT2FIS (Mc-CIT2FIS) consists of a CIT2FIS, which realizes Takagi-Sugeno-Kang type inference mechanism, as its cognitive component. A PBL with self-regulation is its metacognitive component. The rules of CIT2FIS employ interval type- (2~q) -Gaussian membership functions that can represent different radial basis functions for different values of (q) . As each sample is presented to the network, the metacognitive component monitors the hinge-loss error and class-specific knowledge potential of the current sample to efficiently decide on what-to-learn, when-to-learn, and how-to-learn it. When a new rule is added or existing rules are updated, the optimal parameters of CIT2FIS corresponding to the minimum of the hinge-loss error function are computed using a PBL algorithm derived using the Wirtinger calculus. The performance of Mc-CIT2FIS is evaluated on a set of benchmark real-valued classification problems from the UCI machine learning repository. A circular transformation is used to convert the real-valued features to the complex-valued features in these problems. The performance comparison and statistical study clearly show the superior classification ability of Mc-CIT2FIS. Finally, the proposed complex-valued network is used to solve a practical human action recognition problem that is represented by complex-valued optical flow-based feature set, and a human emotion recognition problem represented using complex-valued Gabor filter-based features. The performance results on these problems substantiate the superior classification ability of Mc-CIT2FIS.
机译：本文提出了一种复值区间2型神经模糊推理系统（CIT2FIS），并推导了其基于元认知投影的学习（PBL）算法。元认知CIT2FIS（Mc-CIT2FIS）由CIT2FIS组成，该CIT2FIS实现了Takagi-Sugeno-Kang类型推断机制，作为其认知成分。具有自我调节能力的PBL是其元认知成分。 CIT2FIS的规则采用区间类型- （2〜q） -高斯隶属函数，可以表示不同的径向基础函数用于 （q） 的不同值。当每个样本都呈现给网络时，元认知组件会监视当前样本的铰链丢失错误和特定于类的知识潜力，从而有效地决定要学习的内容，何时-学习和如何学习。当添加新规则或更新现有规则时，使用Wirtinger演算推导的PBL算法计算与铰链损耗误差函数的最小值相对应的CIT2FIS最佳参数。 Mc-CIT2FIS的性能是根据UCI机器学习存储库中的一组基准实值分类问题进行评估的。在这些问题中，使用循环变换将实值特征转换为复值特征。性能比较和统计研究清楚地显示了Mc-CIT2FIS的出色分类能力。最后，提出的复杂值网络用于解决以复杂值为基础的光流特征集为代表的实际人类动作识别问题，以及使用复杂值为基础的Gabor滤波器特征为代表的人类情感识别问题。这些问题的性能结果证实了Mc-CIT2FIS的出色分类能力。

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