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The BP algorithm is basically a gradient descent technique that can minimize the network error function.
The minimization loop based on the steepest descent technique is implemented within a line search strategy.
To update these parameters we used mini-batches gradient descent technique.
However, such methods used steepest descent technique to minimize the error function such that it may reach the local minimal.
Using a Gradient Descent technique, we prove exponential convergence of the distributed system and estimation of the parameter.
There are also many methods to solve this optimization problem efficiently, such as the block-coordinate descent technique [31] and the Landweber iterations technique [32].
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An algorithm is derived using gradient descent techniques together with a procedure.
Two distinct methods are utilized for fuzzy modeling, the least squares and the gradient descent techniques.
Coordinate descent techniques [25, 26] are employed to derive local updating recursions that allow sensors to associate with targets.
A combination of factorial experimental design and gradient descent techniques was employed to optimize the amount of the Fenton reagents, resulting in Fe2+ (0.1 mM) and H2O2 (50 mM).
Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering.
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