Parameter Estimation of the FitzHugh–Nagumo Neuron Model Using Integrals Over Finite Time Periods

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Antonio Concha ◽  
Rubén Garrido
Keyword(s):  
Parameter Estimation ◽  
Membrane Potential ◽  
Finite Time ◽  
Neuron Model ◽  
Wavelet Denoising ◽  
Measurement Noise ◽  
Identification Method ◽  
Identification Technique ◽  
Time Periods ◽  
Derivatives Of

This paper proposes two methodologies for estimating the parameters of the FitzHugh–Nagumo (FHN) neuron model. The identification procedures use only measurements of the membrane potential. The first technique is named the identification method based on integrals and wavelets (IMIW), which combines a parameterization based on integrals over finite time periods and a wavelet denoising technique for removing the measurement noise. The second technique, termed as the identification method based only on integrals (IMOI), does not use any wavelet denoising technique and attenuates the measurement noise by integrating the IMIW parameterization two times more over finite time periods. Both procedures use the least squares algorithm for estimating the FHN parameters. Integrating the FHN model over finite time periods allows eliminating the unmeasurable recovery variable of this model, thus obtaining a parameterization based on integrals of the measurable membrane potential variable. Unlike an identification technique recently published, the proposed methods do not rely on the time derivatives of the membrane potential and are not limited to continuously differentiable input current stimulus. Numerical simulations show that both the IMIW and IMOI have a good and a similar performance, however, the implementation of the latter is simpler than the implementation of the former.

Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms

2019 ◽  
Vol 87 ◽  
pp. 174-186 ◽  
Author(s):  
Shubo Wang ◽  
Jing Na ◽  
Haisheng Yu ◽  
Qiang Chen
Keyword(s):  
Parameter Estimation ◽  
Finite Time ◽  
Time Parameter ◽  
Performance Control

scholarly journals Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 606
Author(s):  
Alaa Jamal ◽  
Raphael Linker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Sequential Estimation ◽  
Measurement Noise ◽  
Genetic Operators ◽  
True Value

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

Drag Coefficient Identification from Flight Data via Optimal Observer

2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Drag Coefficient ◽  
Dynamic Equation ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Measurement Noise ◽  
Estimation Technique ◽  
Flight Data ◽  
Coefficient Identification

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

Experimental Robot Identification: Advantages of Combining Internal and External Measurements and of Using Periodic Excitation

2001 ◽  
Vol 123 (4) ◽  
pp. 630-636 ◽  
Author(s):  
Walter Verdonck ◽  
Jan Swevers ◽  
Jean-Claude Samin
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Effective Means ◽  
Base Plate ◽  
Experimental Results ◽  
Parameter Estimates ◽  
Periodic Excitation ◽  
Identification Method ◽  
Reaction Forces ◽  
Improved Model

This paper discusses the advantages of using periodic excitation and of combining internal and external measurements in experimental robot identification. This discussion is based on the robot identification method developed by Swevers et al., a method that is recognized by industry as an effective means of robot identification that is frequently used, Hirzinger, G., Fischer, M., Brunner, B., Koeppe, R., Otter, M., Grebenstein, M., and Schafer, I, 1999, “Advances is Robotics: The DLR Experiment,” The International Journal of Robotics Research, Vol. 18, No. 11, pp. 1064–1087 [3]. Experimental results on a KUKA IR 361 show that the periodicity of the robot excitation is a key element of this method. Nonperiodic robot excitation complicates the signal processing preceding the parameter estimation, often yielding less accurate parameter estimates. An extension of this identification method combines internal and external measurements, Chenut, X., Samin, J. C., Swevers, J., and Ganseman, C., 2000, “Combining Internal and External robot Models for improved Model Parameter Estimation,” Mechanical Systems and Signal Processing. Vol. 14, No. 5, pp. 691–704 [4], yielding robot models that allow to accurately predict the actuator torques and the reaction forces/torques of the robot on its base plate, which are both important for the path planning. This paper presents and critically discusses the first experimental results obtained with this method.

scholarly journals A Composite Guidance Law for Suppressing Measurement Noise of LOS Angular Rate

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Feng Chen ◽  
Guangjun He ◽  
Qifang He
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Disturbance Observer ◽  
Sliding Mode ◽  
Angular Rate ◽  
Measurement Noise ◽  
Terminal Sliding Mode ◽  
Guidance Law ◽  
Fast Terminal Sliding Mode ◽  
Low Altitude

To effectively intercept a low-altitude target in clutter background, a nonsingular fast terminal sliding mode guidance law is designed. The designed guidance law can fully exploit the fast convergence characteristics of linear sliding mode control and the finite-time-convergent characteristics of terminal sliding mode control to ensure that the line-of-sight (LOS) angle converges to a desired angle in a limited time at a faster rate. Utilizing the smooth switching characteristics of the hyperbolic tangent function similar to the saturation function, a finite-time-convergent differentiator is designed. Meanwhile, a new finite-time-convergent disturbance observer designed on the tracking differentiator can effectively track the ideal LOS angular rate, suppress the measurement noise, and make a smooth estimation of the target maneuvering acceleration in clutter background. Combining the estimated value of the disturbance observer, the sign function with switch coefficient is introduced to design a composite nonsingular fast terminal sliding mode guidance law. The simulation results show that the composite guidance law can not only effectively suppress the measurement noise of the LOS angular rate and improve the accuracy of low-altitude target intercepting, but also greatly reduce the energy consumption in the interception process.

scholarly journals Modulation of Astrocytes on Mode Selection of Neuron Firing Driven by Electromagnetic Induction

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Zhongquan Gao ◽  
Zhixuan Yuan ◽  
Zuo Wang ◽  
Peihua Feng
Keyword(s):  
Membrane Potential ◽  
Resting State ◽  
Electromagnetic Induction ◽  
Neuron Model ◽  
Mode Selection ◽  
Neuron Firing ◽  
Two Factors ◽  
Selection Of ◽  
Strong Ability

Both of astrocytes and electromagnetic induction are magnificent to modulate neuron firing by introducing feedback currents to membrane potential. An improved astro-neuron model considering both of the two factors is employed to investigate their different roles in modulation. The mixing mode, defined by combination of period bursting and depolarization blockage, characterizes the effect of astrocytes. Mixing mode and period bursting alternatively appear in parameter space with respect to the amplitude of feedback current on neuron from astrocyte modulation. However, magnetic flux obviously plays a role of neuron firing inhibition. It not only repels the mixing mode but also suppresses period bursting. The mixing mode becomes period bursting mode and even resting state when astrocytes are hyperexcitable. Abnormal activities of astrocytes are capable to induce depolarization blockage to compose the mixing mode together with bursting mode. But electromagnetic induction shows its strong ability of inhibition of neuron firing, which is also illustrated in the bifurcation diagram. Indeed, the combination of the two factors and appropriate choice of parameters show the great potential to control disorder of neuron firing like epilepsy.

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