Modeling the Behavior of Amphiphilic Aqueous Solutions

Two types of predictive models based on artificial neural networks (ANN) and quadratic regression model developed in our laboratory will be summarized in this book chapter. Both models were developed to predict the density, speed of sound, kinematic viscosity and surface tension of amphiphilic aqueous solutions. These models were developed taking into account the concentration, the number of carbons and the molecular weight values. The experimental data were compiled from literature and included different surfactants: i) hexyl, ii) octyl, iii) decyl, iv) tetradecyl and v) octadecyl trimethyl ammonium bromide. Neural models present better adjustment values, with R2 values above 0.902 and AAPD values under 2.93% (for all data), than the quadratic regression models. Finally, it is concluded that the quadratic regression and the neural models can be powerful prediction tools for the physical properties of surfactants aqueous solutions.

Tuning Artificial Neural Network Controller Using Particle Swarm Optimization Technique for Nonlinear System

This chapter proposes an optimization technique of Artificial Neural Network (ANN) controller, of single-input single-output time-varying discrete nonlinear system. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in ANN to avoid any possibilities from local extreme condition. Further, a PSO based neural network controller is also developed to be integrated with the designed system to control a nonlinear systems. The simulation results of an example of nonlinear system demonstrate the effectiveness of the proposed approach using Particle Swarm Optimization approach in terms of reduced oscillations compared to classical neural network optimization method. MATLAB was used as simulation tool.