The multi-criteria predictive control of nonlinear dynamical systems based on Artificial Neural Networks (ANNs) and genetic algorithms (GAs) are considered. The (ANNs) are used to determine process models at each operating level; the control action is provided by minimizing a set of control...

Energy production is considered as one of the key indicators for economic development. It is vital to improve the renewable energy production for global sustainability, while leveraging the national resources. This study is contributing to the demonstration of using genetic algorithms (GA) in...

The Proportional, Integral and Derivative (PID) controllers are widely used in industrial applications. Their popularity comes from their robust performance and also from their functional simplicity.

Empirical modeling of the industrial antibiotic fed-batch fermentation process is discussed in this paper. Several methods including neural networks, genetic algorithms and feature selection are combined with prior knowledge in the research methodology. A linear model, a radial basis function...

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search...

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular...

The aim of this work is the development of an optimization model in order to minimize the cost of Leaf Community microgrid. This cost is a sum of energy cost and the maintenance cost of the energy storage system (ESS). The developed objective function is constrained and the problem here is...

In this methodological work I explore the possibility of explicitly modelling expec-tations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of internal model . The last part...