Name
Abstract | ||
|---|---|---|
Spiking neural P (SNP) systems are a class of neural-like membrane computing models that are abstracted by applying the mechanisms of spiking neurons. In SNP systems, each spiking neuron has three characteristics: (i) internal state, (ii) spike consumption, and (iii) spike generation. These three characteristics are used to form a parameterised nonlinear SNP system, which has a nonlinear spiking mechanism, three nonlinear gate functions, and trainable parameters. Based on the parameterised nonlinear SNP system, we develop a novel variant of long short-term memory (LSTM), called the LSTM-SNP model. LSTM-SNP is a recurrent-type model that can process sequential data. Time series forecasting problems are used to conduct a case study. Five benchmark time series are used to evaluate the proposed LSTM-SNP model and compare seven state-of-the-art prediction models and five baseline prediction models. The comparison results show the effectiveness of the proposed LSTM-SNP model for time series forecasting. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2021.107656 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Spiking neural P systems,Spiking neurons,Long short-term memory,Time series forecasting | Journal | 235 |
ISSN | Citations | PageRank |
0950-7051 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Liu | 1 | 1 | 0.35 |
| Lifan Long | 2 | 1 | 1.37 |
| Qian Yang | 3 | 2 | 4.09 |
| Hong Peng | 4 | 321 | 28.87 |
| Jian Wang | 5 | 25 | 26.58 |
| Xiaohui Luo | 6 | 1 | 1.37 |