The phrase "a suitable error" is correct and usable in written English.
It can be used when discussing an error that is appropriate or acceptable in a given context, such as in programming or problem-solving scenarios.
Example: "After reviewing the code, I found a suitable error that could be easily fixed without affecting the overall functionality."
Alternatives: "an appropriate mistake" or "a fitting error".
This depends on the choice of a suitable error measure.
Back propagation is based on minimization of a suitable error or cost function.
A suitable error model is derived based on signals filtered from plant input and output.
However, this property is often disregarded in developing a watermarking scheme, assuming a reliable (operating) environment where communication errors are 'negligible' and can be managed, for example, by using a suitable error correction code.
However, recent studies have proposed to overcome some of these problems especially an optimization-based workflow that determines an optimal topology that fulfills a suitable error margin (Enab and Ertekin 2014; Enyioha et al. 2014).
The main advantage of this approach is that no statistical models have to be assumed during the design, and if a suitable error function is used during training, a good approximation to the optimal NP detector is obtained[1].
The receiver then applies a discrete Fourier transform (DFT) to generate estimate of c. Finally it extracts the block of input bits by applying a suitable error-correction algorithm.
Further relevant between and within subject factors were crossed with group, maze and order, and all suitable error terms for a mixed model included, following Newman et al. [24].
In order to formulate and solve this prescribed performance tracking problem on SE(3), we first select a suitable tracking error vector and prescribed performance bound which characterizes the minimum convergence rate and maximum overshoot of the tracking error vector.
The convergence of the reduced order model with increasing number of basis functions is addressed in a suitable H1 error norm.
We also show that the observation error is ultimately bounded using a Lyapunov approach without having recourse to the usual strictly positive real (SPR) condition or a suitable observation error filtering.
