Topological design of brain-like reservoir based on scale-free network

doi:10.3785/j.issn.1008-973X.2025.07.006

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (7): 1385-1393 DOI: 10.3785/j.issn.1008-973X.2025.07.006

Topological design of brain-like reservoir based on scale-free network

Xuanyun LIU1(

),Ying YAN1,Zhiyong YU1,2,3,Fangwan HUANG1,2,3,*(

)

1. College of Computer and Data Sciences, Fuzhou University, Fuzhou 350108, China
2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108, China
3. Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China

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Abstract

To optimize the reservoir design of echo state networks (ESN), the Chung-Lu (CL) construction algorithm based on random matrix theory was applied to generate flexible and efficient scale-free networks. In response to the degree deviation that occurs during the construction process, random pruning or degree pruning was used to improve the scale-free network. The robustness of the reservoir was improved by simulating random or targeted attacks. Experimental results show that the CL algorithm with a pruning mechanism not only constructs a scale-free network with power-law property, but also has significantly better construction speed and prediction performance than the baseline algorithms, and the effect of random pruning was better than that of degree pruning. Compared with the optimal result of the baseline algorithm, the running time and prediction error of ESN constructed by the CL algorithm based on random pruning were at least reduced by 14.2% and 10.6%.

Key words： scale-free network brain-like reservoir pruning mechanism echo state network time series prediction

Received: 03 September 2024 Published: 25 July 2025

CLC:

TP 393

Fund: 国家自然科学基金资助项目（62332014）；福建省引导性资助项目（2020H0008）；福建省促进海洋与渔业产业高质量发展专项资金（FJHYF-ZH-2023-02）.

Corresponding Authors: Fangwan HUANG E-mail: 3167131927@qq.com;hfw@fzu.edu.cn

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Cite this article:

Xuanyun LIU,Ying YAN,Zhiyong YU,Fangwan HUANG. Topological design of brain-like reservoir based on scale-free network. Journal of ZheJiang University (Engineering Science), 2025, 59(7): 1385-1393.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.07.006 OR https://www.zjujournals.com/eng/Y2025/V59/I7/1385

基于无标度网络的类脑储备池拓扑设计

为了优化回声状态网络（ESN）储备池的设计，应用基于随机矩阵理论的Chung-Lu（CL）构造算法生成灵活且高效的无标度网络. 针对在构建过程中出现的度值偏差，使用随机剪枝或度值剪枝改进无标度网络. 通过模拟随机攻击或针对性攻击来提高储备池的鲁棒性. 实验结果表明，加入剪枝机制的CL算法构造了具有幂律性质的无标度网络，构建速度和预测性能均明显优于基线算法，随机剪枝的效果优于度值剪枝. 相比基线算法的最优结果，基于随机剪枝的CL算法构建的ESN的运行时间和预测误差最少降低了14.2%和10.6%.

关键词： 无标度网络, 类脑储备池, 剪枝机制, 回声状态网络, 时间序列预测

Fig.1 Node degree distribution of scale-free network generated by improved Chung-Lu algorithm

Fig.2 First 10 000 data points of MSO dataset (step size is 0.1)

Fig.3 Node degree distribution after pruning by different mechanisms with different power-law indexes

Fig.4 Comparison between ideal average degree and real average degree under different mechanisms

Fig.5 Comparison of rich club coefficient with different power law indexes under different mechanisms

Tab.1 Sparsity of reservoir based on Chung-Lu algorithm

Tab.2 Mean squared error of echo state networks based on Chung-Lu algorithm

Tab.3 Mean absolute percentage error of echo state networks based on Chung-Lu algorithm

Tab.4 Running time of echo state networks based on Chung-Lu algorithm

Tab.5 Comparison of prediction performance of echo state networks constructed by different methods under different datasets


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