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PANFIS: A Novel Incremental Learning Machine

机译：PANFIS：一种新型的增量学习机

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摘要

Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.
机译：现实世界系统中的大多数动力学都是通过变化和漂移来汇编的，而无所不在的神经模糊系统很难克服这些变化。但是，在非平稳环境中学习需要具有高度灵活性的系统，该系统能够根据系统中包含的非线性程度自主地组装其规则库。在实践中，仅从完整训练数据的小快照中受益，就可以执行规则增长和修剪，从而将计算负载和内存需求截断到较低的水平。为此目的，本文提出了一种新颖的算法，即基于模糊推理系统（PANFIS）的简约网络。 PANFIS可以使用空规则库从头开始学习过程。模糊规则可以通过模糊规则和随后注入的数据的统计贡献进行拼接和排除。可以暗示相同的模糊集并将其融合为一个模糊集，以追求透明的规则库，从而提升人类的可解释性。所提出的PANFIS的学习和建模性能已使用来自实际或综合数据集中的几个基准问题进行了数值验证。验证包括与最新发展的神经模糊方法进行比较，并证明我们的新方法在预测保真度和模型复杂性方面可以竞争甚至在某些情况下甚至优于这些方法。

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