Antonelo, E.A., Schrauwen, B.: On learning navigation behaviors for small mobile robots with reservoir computing architectures. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 763–780 (2015)
Antonelo, E.A., Flesch, C., Schmitz, F.: Reservoir computing for detection of steady state in performance tests of compressors. Neurocomputing (in press)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)
Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Green, R.C.: High performance computing for detection of electricity theft. Int. J. Electr. Power Energy Syst. 47, 21–30 (2013)
Glauner, P., Meira, J., Valtchev, P., State, R., Bettinger, F.: The challenge of nontechnical loss detection using artificial intelligence: a survey. Int. J. Comput. Intell. Syst. (IJCIS) 10(1), 760–775 (2017)
Heckman, J.J.: Sample selection bias as a specification error. Econometrica 47(1), 153–161 (1979). http://www.jstor.org/stable/1912352
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report GMD Report 148, German National Research Center for Information Technology (2001)
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304(5667), 78–80 (2004)
Meira, J.A., Glauner, P., Valtchev, P., Dolberg, L., Bettinger, F., Duarte, D., et al.: Distilling provider-independent data for general detection of non-technical losses. In: Power and Energy Conference, Illinois, 23–24 February 2017 (2017)
Schrauwen, B., Warderman, M., Verstraeten, D., Steil, J.J., Stroobandt, D.: Improving reservoirs using intrinsic plasticity. Neurocomputing 71, 1159–1171 (2008)
Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007)