On the quantification of dynamics in reservoir computing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5768 LNCS, Issue PART 1, 2009, Pages 985-994

  Verstraeten, David ^a   Schrauwen, Benjamin ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; COMPUTATIONALLY EFFICIENT; DYNAMICAL SYSTEMS THEORY; GLOBAL PARAMETERS; JACOBIANS; RESERVOIR COMPUTING; RESERVOIR DYNAMICS; SIMPLE LINEAR REGRESSION; SPECTRAL RADII; STABILITY PROBLEM; TEMPORAL PROCESSING; UNDERLYING MECHANISM;

BACKPROPAGATION; COMPUTER SCIENCE; DYNAMIC RESPONSE; DYNAMICAL SYSTEMS; DYNAMICS;

RECURRENT NEURAL NETWORKS;

EID: 70350602624     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04274-4_101     Document Type: Conference Paper

References (18)

1. Buehner, M., Young, P.: A tighter bound for the echo state property. IEEE Transactions on Neural Networks 17(3), 820-824 (2006)
2. Csaki, F.: State-space methods for control systems. Akademiai Kiado (1977)
3. Ganguli, S., Huh, D., Sompolinsky, H.: Memory traces in dynamical systems. Proceedings of the National Academy of Sciences 105(48), 18970 (2008)
4. Jaeger, H.: Short term memory in echo state networks. Technical Report GMD Report 152, German National Research Center for Information Technology (2001)
5. Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Advances in Neural Information Processing Systems, pp. 593-600 (2003)
6. Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 308, 78-80 (2004)
7. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531-2560 (2002)
8. Merlet, J.P.: Jacobian, manipulability, condition number, and accuracy of parallel robots. Journal of Mechanical Design 128, 199 (2006)
9. Ozturk, M.C., Xu, D., Principe, J.C.: Analysis and design of echo state networks. Neural Computation 19, 111-138 (2006)
10. Penrose, R.: A generalized inverse for matrices, vol. 51 (1955), pp. 406-413 (1955)
11. Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press, Cambridge (2004)
12. Siewert, U., Wustlich, W.: Echo-state networks with band-pass neurons: Towards generic time-scale-independent reservoir structures. Preliminary Report (October 2007)
13. Vandoorne, K., Dierckx, W., Schrauwen, B., Verstraeten, D., Baets, R., Bienstman, P., Campenhout, J.V.: Toward optical signal processing using photonic reservoir computing. Optics Express 16(15), 11182-11192 (2008)
14. Verstraeten, D., Schrauwen, B., Stroobandt, D.: Reservoir-based techniques for speech recognition. In: Proceedings of the World Conference on Computational Intelligence, pp. 1050-1053 (2006)
15. Verstraeten, D., Schrauwen, B., D'Haene, M., Stroobandt., D.: A unifying comparison of reservoir computing methods. Neural Networks 20, 391-403 (2007)
16. Verstraeten, D., Souza, S.X.-d., Schrauwen, B., Suykens, J., Stroobandt, D., Vandewalle, J.: Pattern classification with CNNs as reservoirs. In: Proceedings of the International Symposium on Nonlinear Theory and its Applications (NOLTA), vol. 9 (2008)
17. White, O.L., Lee, D.D., Sompolinsky, H.: Short-term memory in orthogonal neural networks. Neural Comput. Phys. Rev. Lett. 92, 148102 (2002)
18. Wyffels, F., Schrauwen, B., Verstraeten, D., Stroobandt, D.: Band-pass reservoir computing. In: Hou, Z., Zhang, N. (eds.) Proceedings of the International Joint Conference on Neural Networks, Hong Kong, June, pp. 3203-3208 (2008)