This paper presents real power optimization with load flow using an adaptive Hopfield neural network. In order to speed up the convergence of the Hopfield neural network system, the two adaptive methods, slope adjustment and bias adjustment, were used with adaptive learning rates. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have ...

It is well-known that part of the neural networks capacity determined by their topology and employed training process. How a network should be designed how it updated every time new data acquired, an issue remains open since its usually limited to process trial error, based mainly on experience designer. To address this issue, algorithm provides plasticity recurrent (RNN) applied series forecas...

Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number pixels as training instances. Therefore, it is hard to fully use advantages networks; for example, very layers with large parameters lead overfitting. This paper proposed dynamic wide neural network (DWDNN) HSI ...

In the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. One of the most common types of DG technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone Studies the dynamic behavior andstability of the power grid is of crucial importance. These studies need to know the exact mo...

We propose a simple framework for critic-based training of recurrent neural networks and feedback controllers. We term the critics that are used primitive adaptive critics, since we represent them with the simplest possible architecture (bias weight only). We derive this framework from two main premises. The first of these is a natural similarity between a form of approximate dynamic programmin...