This paper proposes an identification method to estimate the parameters of the FitzHugh-Nagumo (FHN) model for a neuron using noisy measurements available from a voltage-clamp experiment. By eliminating an unmeasurable recovery variable from the FHN model, a parametric second order ordinary differential equation for the only measurable membrane potential variable can be obtained. In the presence of the measurement noise, a simple least squares method is employed to estimate the associated parameters involved in the FHN model. Although the available measurements for the membrane potential are contaminated with noises, the proposed identification method aided by wavelet denoising can also give the FHN model parameters with satisfactory accuracy. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

At present, water shortage in agriculture becomes more and more serious. This situation makes it necessary to develop precision irrigation, which needs to obtain the accurate crop water stress in advance. However, this signal is too weak to be detected easily. Wavelet analysis has been widely used in the signal processing area for almost two decades due to its excellent time-frequency analysis ability. Therefore, the wavelet decomposition and reconstruction technique is applied to reduce the noises of experimental data collected from corn plants in a farmland. Finally, data analysis results show that wavelet denoising is effective to achieve the weak signal extraction.