The training of Neural Networks and particularly Multi-Layer Perceptrons (MLP's) is made by minimizing an error function usually known as "cost function". In our previous works, we apply the Error Entropy Minimization (EEM) algorithm in classification and its optimized version using, as cost function, the entropy of the errors between the outputs and the desired targets of the neural network. O...

Adaptive signal processing theory was born and has lived by exclusively exploiting the mean square error criterion. When we think of the goal of least squares without restrictions of Gaussianity, one has to wonder why an information theoretic error criterion is not utilized instead. After all, the goal of adaptive filtering should be to find the linear projection that best captures the informat...

train energy saving problem investigates how to control train's velocity such that the quantity of energy consumption is minimized and some system constraints are satis ed. on the assumption that the train's weights on different links are estimated by fuzzy variables when making the train scheduling strategy, we study the fuzzy train energy saving problem. first, we propose a fuzzy energy ...

We study the value of the mean entropy production as a function of the level of description. We prove that by coarsening the system, we obtain a lower entropy production. As main example we consider the Lorentz lattice gas and prove that the microscopic entropy production is strictly larger than the one predicted by the Boltzmann equation.

Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to ...