The phrase "additive input" is correct and usable in written English.
It can be used in contexts related to data, processes, or systems where additional information or contributions are being added to an existing set.
Example: "The model's performance improved significantly with the inclusion of additive input from various data sources."
Alternatives: "supplementary input" or "additional input".
We first consider additive input uncertainties and design a high-gain filter to estimate the uncertainty.
The designed controller is still valid when the system has an additive input disturbance which, essentially different from those studied previously, may not be periodic or bounded by any known constant.
With singularities conquered, this ZG neural dynamics is able to solve the tracking-control problem of the modified Lorenz nonlinear system via additive input or mixed inputs (i.e., the mixture of additive and multiplicative inputs).
The tracking-control problem of a special nonlinear system (i.e., the extension of a modified Lorenz chaotic system) with additive input or the mixture of additive and multiplicative inputs is considered in this paper.
Both theoretical analyses and simulative verifications substantiate that the tracking controllers based on the ZG neural dynamics with additive input or mixed inputs not only achieve satisfactory tracking accuracy but also successfully conquer the singularities encountered during the tracking-control process.
The rate law versions shown in Eqs. 1 and 2 assume additive input processing and a linear transcript degradation rate (β ix i) that depends only on the concentration of the target gene's product.
As in [7], the correlation is expressed by an additive input-dependent noise model, W = Y + N, where the correlation noise N ~ ℒ 0, σ y)) is zero-mean Laplacian with standard-deviation σ y), which varies depending on the realization y of the input of the channel, i.e., the side information, namely [7], f N Y n y = 1 σ y 2 e - 2 n σ y (5).
An unusual representation of the model has been chosen considering the components of the wind velocity as additive inputs of the model.
The model optimized the input additives and then maximized the alpha as output.
In this paper, we present a new robust iterative learning control (ILC) design for a class of linear systems in the presence of time-varying parametric uncertainties and additive input/output disturbances.
