Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one of the most challenging problems for machine learning models. To address this challenge, we first propose a nonlinear version of SNP systems, called nonlinear SNP systems with autapses (NSNP-AU systems). In addition to the nonlinear consumption and generation of spikes, the NSNP-AU systems have three nonlinear gate functions, which are related to the states and outputs of the neurons. Inspired by the spiking mechanisms of NSNP-AU systems, we develop a recurrent-type prediction model for chaotic time series, called the NSNP-AU model. As a new variant of recurrent neural networks (RNNs), the NSNP-AU model is implemented in a popular deep learning framework. Four datasets of chaotic time series are investigated using the proposed NSNP-AU model, five state-of-the-art models, and 28 baseline prediction models. The experimental results demonstrate the advantage of the proposed NSNP-AU model for chaotic time series forecasting.

中文翻译：

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具有自动预测混沌时间序列的非线性尖峰神经系统


尖峰神经P（SNP）系统是一类分布式并行类神经计算模型，受到尖峰神经元机制的启发，是第三代神经网络。混沌时间序列预测是机器学习模型最具挑战性的问题之一。为了应对这一挑战，我们首先提出了一种非线性版本的 SNP 系统，称为具有自动功能的非线性 SNP 系统（NSNP-AU 系统）。除了非线性消耗和尖峰的产生之外，NSNP-AU系统还具有三个非线性门函数，​​它们与神经元的状态和输出相关。受 NSNP-AU 系统尖峰机制的启发，我们开发了一种混沌时间序列的循环型预测模型，称为 NSNP-AU 模型。作为循环神经网络（RNN）的新变体，NSNP-AU模型在流行的深度学习框架中实现。使用所提出的 NSNP-AU 模型、五个最先进的模型和 28 个基线预测模型研究了四个混沌时间序列数据集。实验结果证明了所提出的 NSNP-AU 模型在混沌时间序列预测方面的优势。