Besides, our study gives comparable optimal efficiencies to those obtained from previous studies based on gradient-based optimization, but completely different design points (especially for the spline-based morphing), which would indicate that the design space associated with the flapping kinematics is very complex.
The adaptive schemes for the maximum likelihood estimation based on gradient-based optimization methods are, in general, preferable.
The other category is based on gradient-free optimization, such as simultaneous perturbation stochastic approximation, genetic algorithm, particle swarm optimization, and pattern search methods (PSMs).
Respect to other optimization methods, the optimization techniques based on gradient attempt to estimate the gradient of the error surface and proceed to an optimum solution by following the negative direction of this estimated vector.
The algorithm includes adaptation of covariance matrix which is basically an alternative method of traditional Quasi-Newton method for optimization based on gradient method.
The optimization is based on gradient based strategies where the required derivatives are formulated by the adjoint approach.
They are next used as a starting guess for an iterative optimization procedure based on gradient descent where all parameters in the descriptors are allowed to vary.
One category is based on gradient, including finite difference approximation of derivative, adjoint gradient-based methods, and gradient simulator methods.
It is based on gradient descent method.
In order to realize the mobility robustness optimization, a cost function incorporating the unnecessary handovers and wrong handovers is introduced, and then a MRO scheme based on gradient algorithm [18, 19] is proposed for femtocell networks.
