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A neural network structure is given which implements the steepest descent method for minimizing the objective function.
The proposed work also uses optimum steepest descent method for weight parameter update to minimize the sum square error.
For solving the optimization problem, three metaheuristics (Gravitational Search Algorithm, Bat Algorithm and Particle Swarm Optimization) are combined with the Steepest Descent Method for local search capability enhancement.
Second, to speed up the convergence of the learning algorithm and lighten the oscillation, an improved descent method for FIS generation is developed.
In this paper, we propose a hybrid descent method for solving a special case of the well-known network design problem with relays; that is, the two-edge disjoint survivable network design problem with relays.
The above partial derivatives are used in gradient descent method for cost minimization.
Similar(17)
Our proposition provides the extension to deep networks of key properties of gradient descent methods for linear networks, that as, suggested in (1), can be the key to understand generalization.
Moreover, he discussed descent methods for solving the equivalent problem (see Lemma 5 in Section 2).
Employing steepest descent methods for solving (35) may result in very slow convergence, whereas alternative methods may not be applicable when the number of dimensions is large.
Another algorithm, which utilizes efficient coordinate descent methods for fitting the entire lasso or elastic-net regularization path for linear regression, logistic regression and multinomial regression models, was proposed and detailed in [41].
We use the hybrid steepest descent methods for finding a common element of the set of solutions of an equilibrium problem and the set of fixed points of a strict pseudocontraction mapping in the setting of real Hilbert spaces.
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