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Multi-parallel Extreme Learning Machine with Excitatory and Inhibitory Neurons for Regression

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Abstract

Compared with traditional neural networks, extreme learning machine (ELM) shows outstanding performances on speed and computation. Aiming at the problems that ELM needs more hidden layer neurons and meaningful features of data sometimes are sacrificed in order to improve the training speed, a novelty network multi-parallel extreme learning machine with excitatory and inhibitory neurons (MEI-ELM) is proposed based on the idea of biological neurons. In MEI-ELM, (1) A parallel system is introduced to make it more compact and reduce the number of hidden layer neurons. (2) The property of excitatory and inhibitory of biological neuronal for data processing is introduced to improve its performance. Through applying MEI-ELM, ELM, Fast Learning Network (FLN) and Fast Learning Network with Parallel Layer Perceptrons (PLP-FLN) to 11 classical regression problems, it can be obtained that MEI-ELM performs much better than the other methods in generalization and stability.

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References

  1. Seifert Jeffrey W (2004) Data mining: an overview. In: World engineering congress

  2. Green M, Ekelund U, Edenbrandt L et al (2009) Exploring new possibilities for case-based explanation of artificial neural network ensembles. Neural Netw 22:75–81

    Article  Google Scholar 

  3. May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw Off J Int Neural Netw Soc 23:283–294

    Article  Google Scholar 

  4. Bin LI, Yi-Bin LI (2011) Chaotic time series prediction based on ELM learning algorithm. Tianjin Daxue Xuebao 44:701–704

    MathSciNet  Google Scholar 

  5. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  6. Li MB, Meng JE (2006) Nonlinear system identification using extreme learning machine. In: 9th International conference on control, automation, robotics and vision, 2006. ICARCV’06. IEEE, pp 1–4

  7. Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9:541–552

    Article  Google Scholar 

  8. Rong HJ, Huang GB, Sundararajan N et al (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B Cybern A Publ IEEE Syst Man Cybern Soc 39:1067–1072

    Article  Google Scholar 

  9. Han F, Huang DS (2006) Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 69:2369–2373

    Article  Google Scholar 

  10. Rong HJ, Ong YS, Tan AH et al (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72:359–366

    Article  Google Scholar 

  11. Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomptuing 71:3460–3468

    Article  Google Scholar 

  12. Huang G, Song SJ, Gupta JND, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405–2417

    Article  Google Scholar 

  13. Jie Z, Wendong X, Yanjiao L et al (2018) Residual compensation extreme learning machine for regression. Neurocomputing 311:126–136

    Article  Google Scholar 

  14. Lv F, Han M (2019) Hyperspectral image classification based on multiple reduced kernel extreme learning machine. Int J Mach Learn Cybern 6:1–9

    Google Scholar 

  15. Feixiang Zhao ID, Liu Y, Huo K et al (2018) Radar HRRP target recognition based on stacked autoencoder and extreme learning machine. Sensors 18(1):173

    Article  Google Scholar 

  16. Khatab ZE, Hajihoseini A, Ghorashi SA (2018) A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sens Lett 2(1):1–4

    Article  Google Scholar 

  17. Li G, Niu P, Duan X et al (2013) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Appl 24:1683–1695

    Article  Google Scholar 

  18. Caminhas WM, Vieira DAG, Vasconcelos JA (2003) Parallel layer perceptron. Neurocomputing 55:771–778

    Article  Google Scholar 

  19. Li G et al (2017) Fast learning network with parallel layer perceptrons. Neural Process Lett 47:549–564

    Article  Google Scholar 

  20. Caminhas WM, Vieira DAG, Vasconcelos JA (2003) Parallel layer perceptron. Neurocomputing 55:771–778

    Article  Google Scholar 

  21. Uchizono K (1965) Characteristics of excitatory and inhibitory synapses in the central nervous system of the cat. Nature 207(4997):642–643

    Article  Google Scholar 

  22. Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12(1):1–24

    Article  Google Scholar 

  23. Billeh Yazan N, Schaub MT (2018) Feedforward architectures driven by inhibitory interactions. J Comput Neurosci 44(18):63–74

    Article  MathSciNet  Google Scholar 

  24. Yao Mingchen, Li W, Liu Y (2011) Double parallel extreme learning machine. Energy Procedia 13:7413–7418

    Article  Google Scholar 

  25. Corinto F et al (2011) Synchronization in networks of FitzHugh–Nagumo neurons with memristor synapses. In: European conference on circuit theory and design IEEE

  26. Gerard R (1941) The interaction of neurones. Ohio J 41:160–172

    Google Scholar 

Download references

Acknowledgements

Project supported by the National Natural Science Foundation of China (Grant No. 61403331), Program for the Top Young Talents of Higher Learning Institutions of Hebei (Grant No. BJ2017033), Natural Science Foundation of Hebei Province (Grant No. F2016203427), China Postdoctoral Science Foundation (Grant No. 2015M571280)

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  1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Guoqiang Li & Junnan Zou

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  1. Guoqiang Li
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  2. Junnan Zou
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Correspondence to Junnan Zou.

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Li, G., Zou, J. Multi-parallel Extreme Learning Machine with Excitatory and Inhibitory Neurons for Regression. Neural Process Lett 51, 1579–1597 (2020). https://doi.org/10.1007/s11063-019-10160-3

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