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Fuzzy Associative Conjuncted Maps Network

机译：模糊联想联合地图网络

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摘要

The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form if-then rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.
机译：模糊关联联合映射（FASCOM）是一个模糊神经网络，可将非线性相关输入和输出的数据相关联。在网络中，每个输入或输出维都由一个特征图表示，该特征图被划分为模糊或明晰集。然后将这些模糊集合并以形成先例和结果，然后将其关联以形成if-then规则。关联存储器通过离线批处理模式学习过程进行编码，该过程由三个连续的阶段组成。最初的无监督隶属函数初始化阶段通过基于统一的信息密度分配隶属函数，从我们大脑中的感觉图的组织中获得灵感。接下来，有监督的Hebbian学习对输入和输出节点之间的突触权重进行编码。最后，有监督的错误减少阶段将对网络进行微调，从而可以发现每个输入维度对编码内存中输出特征空间的影响程度的变化。在一系列实验中，我们表明学习过程中的每个阶段都对最终的预测准确性做出了重要贡献。使用玩具问题和真实世界数据进行的进一步实验证明，在与其他著名体系结构进行基准比较时，在非线性估计的准确性方面具有显着优势，并且展示了网络适合于对真实世界应用程序进行分析和预测，例如交通密度预测，如图所示在本文中。

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