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Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing

机译：Neo-Fuzzy编码器及其大数据处理的自适应学习

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In the paper a two-layer encoder is proposed. The nodes of encoder under consideration are neo-fuzzy neurons, which are characterised by high speed of learning process and effective approximation properties. The proposed architecture of neo-fuzzy encoder has a two-layer bottle neck” structure and its learning algorithm is based on error backpropagation. The learning algorithm is characterised by a high rate of convergence because the output signals of encoder’s nodes (neo-fuzzy neurons) are linearly dependent on the tuning parameters. The proposed learning algorithm can tune both the synaptic weights and centres of membership functions. Thus, in the paper the hybrid neo-fuzzy system-encoder is proposed that has essential advantages over conventional neurocompressors.
机译：本文提出了一种两层编码器。正在考虑的编码器节点是新模糊神经元，其特征在于学习过程的高速性和有效的近似特性。所提出的新模糊编码器的体系结构具有两层瓶颈结构，其学习算法基于误差反向传播。该学习算法的特点是收敛速度快，因为编码器节点（新模糊神经元）的输出信号线性依赖于调整参数。所提出的学习算法可以调整突触权重和隶属度函数的中心。因此，在本文中，提出了混合新模糊系统编码器，该编码器具有优于传统神经压缩器的基本优点。

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