Nonlinear Spiking Neural Systems With Autapses for Predicting Chaotic Time Series
IEEE TRANSACTIONS ON CYBERNETICS(2024)
Abstract
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.
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Key words
Predictive models,Time series analysis,Forecasting,Neurons,Computational modeling,Adaptation models,Biological system modeling,Chaotic time series forecasting,nonlinear spiking neural P (SNP) systems with autapses,prediction model,recurrent-type neuron
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