Automatic design of conventional distillation column sequence by genetic algorithm

2009 ◽  
Vol 87 (3) ◽  
pp. 477-492 ◽  
Author(s):  
Ramin Bozorgmehry Boozarjomehry ◽  
Ali Pourahmadi Laleh ◽  
William Y. Svrcek
Keyword(s):  
Genetic Algorithm ◽  
Distillation Column ◽  
Automatic Design

Comparator Synthesis Using Distributed Genetic Algorithm and HSPICE Optimization

2019 ◽  
Vol 888 ◽  
pp. 17-28
Author(s):  
Nobukazu Takai ◽  
Kento Suzuki ◽  
Yoshiki Sugawara
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Design Method ◽  
Automatic Design ◽  
Circuit Topology ◽  
Target Value ◽  
Distributed Genetic Algorithm ◽  
Better Than

In this paper, we propose an automatic design method that determines comparator topology and satisfies desired specification of the comparator by combining distributed genetic algorithm and HSPICE optimization function.In the comparator synthesis, new topology is created using known circuit topology information.After creating the topology, optimization of element values of the comparator is executed by distributed genetic algorithm and HSPICE optimization.As a target value example, specification of HA163S02 is used.Simulation results indicate that the proposed method can design the comparator despite the number of specifications and elements of circuit increase compared to the conventional methods.Furthermore, the performance of the automatic designed comparator is better than that of conventional comparators.

Automatic design of inductive FSSs using the genetic algorithm and the MoM/BI-RME analysis

2002 ◽  
Vol 1 ◽  
pp. 91-93 ◽  
Author(s):  
M. Bozzi ◽  
G. Manara ◽  
A. Monorchio ◽  
L. Perregrini
Keyword(s):  
Genetic Algorithm ◽  
Automatic Design

scholarly journals Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm

2017 ◽  
Author(s):  
Kalpana R. ◽  
Harikumar Kandath ◽  
Senthilkumar J. ◽  
Balasubramanian G. ◽  
Abhay S. Gour
Keyword(s):  
Genetic Algorithm ◽  
Linear Matrix Inequalities ◽  
Simulation Study ◽  
Output Feedback ◽  
Distillation Column ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Static Output Feedback ◽  
Matrix Inequalities ◽  
Static Output

The current work addresses the control of two-input two-output (TITO) Wood and Berry model of a binary distillation column. The controller design problem is formulated in terms of multivariable H∞ control synthesis. The controller structure takes the form of simplest static output feedback (SOF) control. The controller synthesis is performed using a hybrid approach of blending linear matrix inequalities (LMI) and genetic algorithm (GA). The performance of the static output feedback controller is compared with three other controllers designed for Wood and Berry model available in the literature. The first simulation study is performed for the case of tracking a unit step command in the presence of a step change in output disturbance. A second simulation study is performed for rejecting a change in sinusoidal output disturbance.

An Evolutionary Framework for Nonlinear Time-Series Prediction with Adaptive Gated Mixtures of Experts

2011 ◽  
pp. 114-138
Author(s):  
André L.V. Coelho ◽  
Clodoaldo A.M. Lima ◽  
Fernando J. Von Zuben
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Intelligent System ◽  
Time Series Prediction ◽  
Integrated Approach ◽  
Nonlinear Time Series ◽  
Learning Problems ◽  
Automatic Design ◽  
Hybrid Intelligent System

A probabilistic learning technique, known as gated mixture of experts (MEs), is made more adaptive by employing a customized genetic algorithm based on the concepts of hierarchical mixed encoding and hybrid training. The objective of such effort is to promote the automatic design (i.e., structural configuration and parameter calibration) of whole gated ME instances more capable to cope with the intricacies of some difficult machine learning problems whose statistical properties are time-variant. In this chapter, we outline the main steps behind such novel hybrid intelligent system, focusing on its application to the nontrivial task of nonlinear time-series forecasting. Experiment results are reported with respect to three benchmarking time-series problems, and confirmed our expectation that the new integrated approach is capable to outperform, both in terms of accuracy and generalization, other conventional approaches, such as single neural networks and non-adaptive, handcrafted gated MEs.

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