Circadian Phase Control - Genetic Algorithm and Non-linear Control Approach

This paper deals with the phase control for Neurospora circadian rhythm. The nonlinear control, given by tuning the parameters (considered as controlled variables) in Neurospora dynamical model, allows the circadian rhythms tracking a reference one. When there are many parameters (e.g. 3 parameters in this paper) and their values are unknown, the adaptive control law reveals its weakness since the parameters converging and control objective must be guaranteed at the same time. We show that this problem can be solved using the genetic algorithm for parameters estimation. Once the unknown parameters are known, the phase control is performed by chaos synchronization technique.

A Comparative Analysis Between Genetic Algorithms and Complex Nonlinear Least Squares on Electrical Impedance Characterization

This paper compares the performance of the complex nonlinear least squares algorithm implemented in the LEVM/LEVMW software with the performance of a genetic algorithm in the characterization of an electrical impedance of known topology. The effect of the number of measured frequency points and of measurement uncertainty on the estimation of circuit parameters is presented. The analysis is performed on the equivalent circuit impedance of a humidity sensor.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Genetic Algorithms for Parameter Estimation in Circadian Model

This paper proposes an algorithm to estimate two parameter values vs, transcription of frq gene, and vd, maximum rate of FRQ protein degradation for an existing 3rd order Neurospora model in literature. Details of the algorithm with simulation results are shown in this paper.