Node k can maximize its utility by minimizing the cost given by (7).
Then, a model-free optimal control is designed to minimize the given cost functional by ADP technology, which combines a similar Q-learning method with a value iteration (VI) algorithm, using only the measured input/output data.
At the initialization, the PD is bypassed, and the PD coefficients are obtained by minimizing the error signal given by (13).
We learn a linear classifier i.e., f(x i )= w T x i + b, by minimizing the loss function given in Eq. 5, using stochastic gradient descent.
In [21], a generalized prediction step, viewed as a mapping function, is optimized by minimizing the detail signal energy given the pixel value probability conditioned to its neighbor pixel values.
For the conventional Bayesian spectral amplitude estimation methods [4 8], the speech spectral amplitude estimation ( {widehat{X}}_k ) is obtained by minimizing the expectation of a given cost function ( Cleft({X}_k,{widehat{X}}_kright) ), which can be defined as follows: {widehat{X}}_k= arg min Eleft{Cleft({X}_k,{widehat{X}}_kright)right} (3)where E denotes the statistical expectation.
By analyzing the advantages and disadvantages of the existing Lyapunov control, we propose that the quantum state transfer can be accelerated by minimizing the Lyapunov function, and give three optimized control field design methods.
Alternatively, a variation is given by minimizing the total risk under the terminal return constraint.
For signalling problems above varying bottom we show how to optimize the wave number of one or more Airy functions as vertical profiles, by minimizing the kinetic energy functional for the given influx signal.
