scholarly journals Evolutionary Optimization Of Chaos Control - A New Approach

2008 ◽  
Author(s):  
R. Senkerik ◽  
I. Zelinka
Keyword(s):  
Chaos Control ◽  
Evolutionary Optimization ◽  
New Approach

A New Approach to Compensate Oscillations of Path-Linked Presses Caused by Inertial Forces

2016 ◽  
Vol 1140 ◽  
pp. 361-368
Author(s):  
Stefan Hilscher ◽  
Richard Krimm ◽  
Bernd Arno Behrens
Keyword(s):  
Conceptual Design ◽  
Optimization Algorithm ◽  
Evolutionary Optimization ◽  
Inertial Forces ◽  
New Approach ◽  
Virtual Testing ◽  
Linear Motors ◽  
Control Signals ◽  
The Press ◽  
Mass Balancing

Presses with mechanical linkages based on levers between motor and ram (path-linked presses) tend to oscillate due to inertial forces as a consequence of the drive parts motion.In this publication a new approach for a mass-balancing system is presented. This system allows to generate the optimal compensation forces needed to counteract the inertial forces by means of four linear motors. The control signals for the linear motors are specified by an evolutionary optimization algorithm, which operates on the base of measured accelerations of the press frame. The control signals of the linear motors are created in a way that the machines oscillations are reduced to a minimum. This way the presented mass-balancing system adapts itself automatically to varying conditions during the operation of the machine, such as a tool change or a varying stroke rate.In particular, the present publication provides the results of the conceptual design and the virtual testing of this approach, which has been mainly carried out with the help of multiple-body simulations.

A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization

2010 ◽  
Vol 36 (1) ◽  
pp. 242-269 ◽  
Author(s):  
Jeong-Gwan Kang ◽  
Sunhyo Kim ◽  
Su-Yong An ◽  
Se-Young Oh
Keyword(s):  
Evolutionary Optimization ◽  
Map Building ◽  
Model Learning ◽  
New Approach ◽  
Implicit Model

Evolution and Learning Mediated by Differences in Developmental Timing

2007 ◽  
Vol 11 (8) ◽  
pp. 905-913 ◽  
Author(s):  
Kei Ohnishi ◽  
◽  
Masato Uchida ◽  
Yuji Oie ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Cycle Time ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Optimization Process ◽  
Developmental Timing ◽  
New Approach ◽  
Linkage Information ◽  
The Difference ◽  
Evolution Speed

The present paper introduces a mutation-based evolutionary algorithm that evolves genes to regulate the developmental timings of phenotypic values. For each generation, an individual in the evolutionary algorithm time-sequentially generates a given number of entire phenotypes before finishing its life. Each gene represents a cycle time of changing probability for determining its corresponding phenotypic value, which is an indicator of developmental timing. In addition, the algorithm has a learning mechanism such that, during the lifetime of an individual, genes representing a long cycle time can change the probability of adaptation more easily than genes representing a short cycle time. Therefore, if the diversity of the genes is maintained, it can be expected that the algorithm provides a different evolution speed to each phenotypic value. The present paper also discusses a new approach to depicting an evolutionary optimization process. An evolutionary optimization process involves the identification of linkage between variables, and therefore, network structures formed using the identified linkage information determine how the evolutionary algorithm solves a given optimization problem. The proposed approach regards an evolutionary optimization process as a change in the network topology that emerges in the process of linkage identification. The simulation results indicate that evolution and learning mediated by the difference in developmental timing helps to sequentially solve hard uniformly-scaled bit optimization problems with linkage between variables.

scholarly journals A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yingxia Chen ◽  
Guixu Zhang
Keyword(s):  
Performance Metrics ◽  
Spatial Information ◽  
Evolutionary Optimization ◽  
Transformation Model ◽  
New Approach ◽  
Sensing Applications ◽  
High Quality Image ◽  
Fused Image ◽  
Remote Sensing Applications ◽  
A New Technique

In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan-sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity-hue-saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state-of-the-art techniques according to the performance metrics.

scholarly journals A new approach on the modelling, chaos control and synchronization of a fractional biological oscillator

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ali Saleh Alshomrani ◽  
Malik Zaka Ullah ◽  
Dumitru Baleanu
Keyword(s):  
Chaos Control ◽  
Control Method ◽  
Phase Portraits ◽  
New Approach ◽  
Control Scheme ◽  
Biological Oscillator ◽  
The Stability ◽  
Control And Synchronization ◽  
And Control ◽  
Active Control Method

AbstractThis research aims to discuss and control the chaotic behaviour of an autonomous fractional biological oscillator. Indeed, the concept of fractional calculus is used to include memory in the modelling formulation. In addition, we take into account a new auxiliary parameter in order to keep away from dimensional mismatching. Further, we explore the chaotic attractors of the considered model through its corresponding phase-portraits. Additionally, the stability and equilibrium point of the system are studied and investigated. Next, we design a feedback control scheme for the purpose of chaos control and stabilization. Afterwards, we introduce an efficient active control method to achieve synchronization between two chaotic fractional biological oscillators. The efficiency of the proposed stabilizing and synchronizing controllers is verified via theoretical analysis as well as simulations and numerical experiments.

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