Evolutionary Optimization of Liquid State Machines for Robust Learning
Zhou Y, Jin Y, Ding J (2019)
In: Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Lu H, Tang H, Wang Z (Eds); Lecture Notes in Computer Science, 11554. Cham: Springer International Publishing: 389-398.
Konferenzbeitrag
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Zhou, Yan;
Jin, YaochuUniBi 
;
Ding, Jinliang
;
Ding, JinliangHerausgeber*in
Lu, Huchuan;
Tang, Huajin;
Wang, Zhanshan
Abstract / Bemerkung
Liquid State Machines (LSMs) are a computational model of spiking neural networks with recurrent connections in a reservoir. Although they are believed to be biologically more plausible, LSMs have not yet been as successful as other artificial neural networks in solving real world learning problems mainly due to their highly sensitive learning performance to different types of stimuli. To address this issue, a covariance matrix adaptation evolution strategy has been adopted in this paper to optimize the topology and parameters of the LSM, thereby sparing the arduous task of fine tuning the parameters of the LSM for different tasks. The performance of the evolved LSM is demonstrated on three complex real-world pattern classification problems including image recognition and spatio-temporal classification.
Erscheinungsjahr
2019
Titel des Konferenzbandes
Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I
Serien- oder Zeitschriftentitel
Lecture Notes in Computer Science
Band
11554
Seite(n)
389-398
Konferenz
16th International Symposium on Neural Networks (ISNN 2019)
Konferenzort
Moscow, Russia
Konferenzdatum
2019-07-10 – 2019-07-12
ISBN
978-3-030-22795-1
eISBN
978-3-030-22796-8
Page URI
https://pub.uni-bielefeld.de/record/2978436
Zitieren
Zhou Y, Jin Y, Ding J. Evolutionary Optimization of Liquid State Machines for Robust Learning. In: Lu H, Tang H, Wang Z, eds. Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Vol 11554. Cham: Springer International Publishing; 2019: 389-398.
Zhou, Y., Jin, Y., & Ding, J. (2019). Evolutionary Optimization of Liquid State Machines for Robust Learning. In H. Lu, H. Tang, & Z. Wang (Eds.), Lecture Notes in Computer Science: Vol. 11554. Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I (pp. 389-398). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-22796-8_41
Zhou, Yan, Jin, Yaochu, and Ding, Jinliang. 2019. “Evolutionary Optimization of Liquid State Machines for Robust Learning”. In Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I, ed. Huchuan Lu, Huajin Tang, and Zhanshan Wang, 11554:389-398. Lecture Notes in Computer Science. Cham: Springer International Publishing.
Zhou, Y., Jin, Y., and Ding, J. (2019). “Evolutionary Optimization of Liquid State Machines for Robust Learning” in Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I, Lu, H., Tang, H., and Wang, Z. eds. Lecture Notes in Computer Science, vol. 11554, (Cham: Springer International Publishing), 389-398.
Zhou, Y., Jin, Y., & Ding, J., 2019. Evolutionary Optimization of Liquid State Machines for Robust Learning. In H. Lu, H. Tang, & Z. Wang, eds. Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Lecture Notes in Computer Science. no.11554 Cham: Springer International Publishing, pp. 389-398.
Y. Zhou, Y. Jin, and J. Ding, “Evolutionary Optimization of Liquid State Machines for Robust Learning”, Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I, H. Lu, H. Tang, and Z. Wang, eds., Lecture Notes in Computer Science, vol. 11554, Cham: Springer International Publishing, 2019, pp.389-398.
Zhou, Y., Jin, Y., Ding, J.: Evolutionary Optimization of Liquid State Machines for Robust Learning. In: Lu, H., Tang, H., and Wang, Z. (eds.) Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Lecture Notes in Computer Science. 11554, p. 389-398. Springer International Publishing, Cham (2019).
Zhou, Yan, Jin, Yaochu, and Ding, Jinliang. “Evolutionary Optimization of Liquid State Machines for Robust Learning”. Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Ed. Huchuan Lu, Huajin Tang, and Zhanshan Wang. Cham: Springer International Publishing, 2019.Vol. 11554. Lecture Notes in Computer Science. 389-398.
Link(s) zu Volltext(en)
Access Level
Closed Access
Google Scholar
ISBN Suche