The phrase "a linear adaptation of" is correct and usable in written English.
It can be used when discussing a version of a work that has been modified to follow a straight or direct format, often in relation to literature, film, or other media.
Example: "The film is a linear adaptation of the novel, maintaining the original plot while simplifying some of the complex themes."
Alternatives: "a straightforward adaptation of" or "a direct adaptation of".
Moreover, the inclusion of a linear adaptation current, as is common for describing spike frequency adaptation, would allow the study of travelling pulses as well as fronts, building on the deterministic approach in [43] and more recently in [44] for understanding cortical waves in epilepsy.
As an example let us compute the speed ( c > 0 ) and stability of a front in the one-dimensional model discussed in Section 2 with the inclusion of a linear adaptation current.
The final Gaussian is defined by and, respectively the mean and the covariance matrix, (ii) adapting the Gaussian components of the state-independent GMM to state-specific data (using MAP) and then merging adapted Gaussians into a unique Gaussian defined by and, (iii) computing and as the parameters of a linear adaptation between Gaussian and Gaussian. .
The final Gaussian is defined by and, respectively the mean and the covariance matrix, adapting the Gaussian components of the state-independent GMM to state-specific data (using MAP) and then merging adapted Gaussians into a unique Gaussian defined by and, computing and as the parameters of a linear adaptation between Gaussian and Gaussian.
In our scheme, we consider a linear adaptation approach as shown in Figure 3b which adjusts the admission criteria according to the variation of network loads.
Here, linear adaptations of the three leg parameters, leg angle, leg length and leg stiffness during late swing phase are assumed.
A linear adaptive regulator with adaptation algorithm designed by the passification approach is proposed.
In this case, the linear adaptation allows the identification of matrix W to within a scaling matrix, which depends on the nonlinearities and the input signals.
For the linear adaptation case (Section 3.1), the adaptive system is composed of a 2x2 matrix W. For the noise we have taken σ0 = 0.001.
This is a typical behavior of the backpropagation algorithm[1] which is fundamentally different from that of the linear adaptation scheme (Figure 6).
