Recurrent neural networks as fundamentally different neural network from feed-forward architectures was investigated for modelling of non linear behaviour of financial markets. Recurrent neural networks could be configured with the correct choice of parameters such as the number of neurons, the number of epochs, the amount of data and their relationship with the training data for predictions of...

When the output of an atomistic simulation (such as the Gillespie stochastic simulation algorithm, SSA) can be approximated as a diffusion process, we may be interested in the dynamic features of the deterministic (drift) component of this diffusion. We perform traditional scientific computing tasks (integration, steady state and closed orbit computation, and stability analysis) on such a drift...

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensembl...

Smart home energy management systems help the distribution grid operate more efficiently and reliably, enable effective penetration of distributed renewable sources. These rely on robust forecasting, optimization, control/scheduling algorithms that can handle uncertain nature demand generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based ...

As research in alternate energy sources is growing, solar radiation catching the eyes of community immensely. Since generation depends on uncontrollable natural variables, without proper forecasting, this source cannot be trusted. For use machine learning algorithms one best choices. This paper proposed an optimized forecasting ensemble model consisting pre-processing and training phases. The p...

Morton-Firth and Bray's stochastic simulator (StochSim) and Gillespie's stochastic simulation algorithm (SSA) are two important methods for stochastic modelling and simulation of biochemical systems. They have been widely applied to different biological problems. A key question is discussed here: Are these two methods equivalent? These two methods are compared using fundamental probability anal...

In this article, we present a not fully connected recurrent network applied to the problem of load forecasting. Although many authors have pointed out that Recurrent Networks were able to modelize NARMAX process (Non linear Auto Regressive Moving Average with eXogeneous variables), we present a constructing scheme for the MA part. In addition we present a modification of the learning step which...