Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm

Liu, Junxiu; Huang, Xingyue; Huang, Yongchuang; Luo, Yuling; Yang, Su

doi:10.1007/978-3-030-30487-4_58

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…However, many of these feature selection methods have limitations in solving ESN output connection optimization problem, and the evolutionary computation algorithms can avoid these limitations. The evolutionary computation algorithm is inspired by the process of natural evaluation, and a series of approaches for the global optimization are proposed based on the study of biota behaviours [17,18]. Genetic algorithm (GA) [19] is one of the popular evolutionary computation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Echo state network optimization using binary grey wolf algorithm

et al. 2020

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The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error.

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Section: Introductionmentioning

confidence: 99%