Abstract
Weak electrical signals in Senecio cruentus were tested by a touching test system of self-made double shields with platinum sensors. Tested data of electrical signals denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting model was set up to forecast the weak signals of all plants in the globe. Testing result shows that it is feasible to forecast the plant electrical signal for a short period. The forecast data is significant and can be used as preferences for the intelligent automatic control system based on the electrical signal adaptive characteristics of plants to achieve the energy saving on the production both greenhouses and or plastic lookum.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, L.Z., Li, H.X., Lin, M., et al.: Analysis of plant electrical signal in the time domain and frequency domain. Journal of China Jiliang University 16(4), 294–298 (2005)
Lou, C.H.: The substance transportation and information transfer during the growth of higher plants (2). Bulletin of Biology 12, 1–3 (1991)
Ren, H.Y., Wang, X.C., Lou, C.H.: The universal existence of electrical signals and its physiological effects in higher plants. Acta Phytophysiologica Sinica 19(1), 97–101 (1993)
Wang, L.Z., Chai, Z.L.: A study on the analyses of the strategic mechanism in ecological adaptability of plant populations by mathematical models and biochemistry, pp. 43–45. Science Press, Beijing (2004)
Wang, Z.Y., Chen, D.S., Huang, L.: Plant physiological status monitoring system and its application in greenhouse. Transactions of the CSAE 16(2), 101–104 (2000)
Guo, Q.S., Su, C.H., Chen, C.R.: Discussion of prediction earthquake mechanism of bioelectric potential of silk tree. Earthquake Research in Shanxi (supplement), 25–27 (1999)
Ding, J.L., Ding, G.Y., Li, H.X., et al.: Studies on the electrical signal of a seedling in Cucumis sativus L. J. of Zhejiang Science and technology College 18(3), 180–184 (2006)
Wang, L.Z., Li, H.X., Lin, M., et al.: Application statistical analysis method in the study of the plant electrical signal. Journal of Jishou University 27(3), 67–70 (2006)
Li, H.X., Wang, L.Z., Li, Q.: Study on electrical signal in Clivia miniata. China Jiliang University 16(1), 62–65 (2005)
Guo, J.Y., Yang, X.L.: Electrical signals in higher plants. Chinese Agri, Science Bulletin 21(10), 188–191 (2005)
Wang, L.Z., Cao, W.X., Ling, L.J.: The determination of weak electrical signal in leaves of Lycoris radiata. Journal of Northwest Normal University 36(2), 62–66 (2000)
Wang, L.Z., Li, Q., Li, D.S., et al.: Analysis of electrical signal in Osmanthus fragrans. In: Proc. of SPIE, The 6th International Symposium on Instrumentation and Control Technology, vol. 6357, 63570N-1-7 (2006)
Li, Q., Wang, L.Z., Li, D.S., et al.: Analysis of electrical signal of three species in Compositae. Journal of China Jiliang University 17(4), 333–336 (2006)
Donoho, D.L.: De-moise by soft-thresholding. IEEE Trans. on IT 3, 327–613 (1995)
Nishida, S.: Automatic detection method of P300 waveform in single sweep records by using a neural network. Med. Eng. Phys. 16, 425 (1994)
Park, J., Sandberg, I.W.: Approximation and radial-basis-function networks. Neural Comp. 5(2), 305–316 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, J., Wang, L. (2010). A Forecast of RBF Neural Networks on Electrical Signals in Senecio Cruentus. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-15615-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15614-4
Online ISBN: 978-3-642-15615-1
eBook Packages: Computer ScienceComputer Science (R0)