The reduction in computational time is achieved by approximating the analytical model with a neural network and by replacing the exhaustive search of the solution space with a genetic algorithm whose objective function relies on a fuzzy logic representation.
In this framework, approximating the model by a linear model (and the posterior distribution by a normal distribution) leads to the well-known A-optimal [ 30, 31] or D-optimal [ 32- 36] experimental designs, which optimize a property of the Fisher information matrix (FIM) at the maximum likelihood estimator.
Longitudinal and lateral slippage occurring during straight maneuvering are derived by approximating the terramechanics slip model.
The plant inverse is learned by approximating the plant "forward model" from x′ c y′ c to x s, y s and then inverting the resulting 2×2 matrix.
The posture neuro-controller (PNC) learns by approximating the specialized inverse velocity model of WMR so as to map planned positions to suitable velocity commands.
The method of obtaining predicted risks by approximating the coefficients of the marginal model from the corresponding conditional ones also worked well.
We further simplified this model by approximating the three weights w i with a geometric sequence with the common ratio α.
The lowest frequency is extracted with a simple and computationally efficient one degree-of-freedom model obtained by approximating the deflection field with the static deflection of a fixed fixed microbeam loaded by a uniformly distributed force.
It seems important that parameter values obtained in Model 2 by approximating the experimental data on gene transcription level ratios (knockout, heat shock, etc).
b Normalized amplitude distribution and estimated distributions Table 1 Kolmogorov distance and χ 2 error by approximating the image of Fig. 1a Statistical model Kolmogorov χ 2 Rayleigh 0.2237 13474.67 Log-normal 0.0211 708.98 Weibull 0.0105 112.95.
