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Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

J. Velasco-Carrau, S. García-Nieto, J. V. Salcedo, R. H. Bishop
2016 Journal of Guidance Control and Dynamics  
The principal contribution is a technique of wind estimation that provides information about the existing wind during flight when no air-data sensors are available.  ...  The estimation technique employs multi-objective optimization algorithms that utilize identification errors to propose the wind-speed components that best fit the dynamic behavior observed.  ...  Acknowledgments The authors would like to thank the Spanish Ministry of Innovation and Science for providing funding through the grant BES-2012-056210, and projects TIN-2011-28082 and ENE-25900.  ... 
doi:10.2514/1.g001294 fatcat:5agxvqvwvzda7ba26av5u63yeu

Inverse Analysis in Civil Engineering: Applications to Identification of Parameters and Design of Structural Material Using Mono or Multi-Objective Particle Swarm Optimization [chapter]

M. Fontan, A. Ndiaye, D. Breysse, P. Castr
2012 Theory and New Applications of Swarm Intelligence  
feasibility of the identification process using the PSO as an efficient tool and, secondly to clearly identify the sources of errors which occur during an identification process.  ...  During the optimization process, the real variables converge to their optima according to the objective functions, whereas each discrete variable randomly traverses its space of definition and consequently  ... 
doi:10.5772/39077 fatcat:zn6mkxoygfa33k2aohafyhmy7e

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

Galih Kusuma Aji, Kenji Hatou, Tetsuo Morimoto
2020 Agroindustrial Journal  
Determining the optimal control strategy of nutrient solution temperature during cultivation could lead to maximize the growth of the plant.  ...  By identifying the process using a dynamic system, the optimal control strategy can be determined.  ...  Therefore, this variable was used as the output variable for identification using LSTM networks. Meanwhile, the input variable is the nutrient solution temperature.  ... 
doi:10.22146/aij.v7i1.60391 fatcat:fvkd6nfif5edvanxzos3yeguo4

Identification of Gaussian mixture model using Mean Variance Mapping Optimization: Venezuelan case

F. M. Gonzalez-Longatt, J. L. Rueda, I. Erlich, D. Bogdanov, W. Villa
2012 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)  
In this paper, an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of GMMs, is presented.  ...  Remarkably, the use of Gaussian mixture model (GMM) has proven to be adequate to tackle the heterogeneity and variability of the statistical distribution of loads.  ...  This means that violation of the variable limits during the search process cannot occur.  ... 
doi:10.1109/isgteurope.2012.6465672 dblp:conf/isgteurope/Gonzalez-LongattREBV12 fatcat:opfbq57wnzhcljh3yilhnaizdy

Vibration Base Identification Of Impact Force Using Genetic Algorithm

R. Hashemi, M.H.Kargarnovin
2007 Zenodo  
The identification problem is formulated as an optimization problem and the genetic algorithm is utilized to solve the optimization problem.  ...  This paper presents the identification of the impact force acting on a simply supported beam.  ...  Furthermore, the changes in the value of decision variables during the optimization process are depicted in Figures 8 and 9 . A.  ... 
doi:10.5281/zenodo.1060538 fatcat:gbbuk7bq6bhpfendim26n3szhu

Development of a methodology for microstructural description

Vanderley de Vasconcelos, Wander L. Vasconcelos
1999 Materials Research  
in evaluating geometric parameters of microstructural features; and difficulties in relating these geometric parameters to process variables.  ...  The methodology was applied on evaluating some topological parameters during sintering process and its results were compared with available experimental data.  ...  response variables and to optimize the experiments.  ... 
doi:10.1590/s1516-14391999000300003 fatcat:wfpszp37yrfcnhiv6lv4akg5cu

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model-Identification Platforms

Arun Pankajakshan, Conor Waldron, Marco Quaglio, Asterios Gavriilidis, Federico Galvanin
2019 Engineering  
The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online-that is, during the execution of experiments.  ...  In this work, a multi-objective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms.  ...  of model predictions for the N y variables that are measured in the process.  ... 
doi:10.1016/j.eng.2019.10.003 fatcat:2b2nljvycfbjboeng4emv22nc4

Improving problem definition through interactive evolutionary computation

2002 Artificial intelligence for engineering design, analysis and manufacturing  
Identified areas requiring further research set the scene for more recent ACDDM work expanding both utility of approach whilst investigating and implementing novel user?  ...  Interactive Evolutionary Design Systems and illustrates these via extensive results from research re various system component interaction with each other and with the user.  ...  can provide the search process for the identification of optimal biomolecule combinations.  ... 
doi:10.1017/s0890060402163050 fatcat:mpoesyjlavgrlizsqipixvxhii

Hybrid design optimization of high voltage pulse transformers for Klystron modulators

Sylvain Candolfi, Philippe Viarouge, Davide Aguglia, Jérôme Cros
2015 IEEE transactions on dielectrics and electrical insulation  
Each intermediate optimal solution is evaluated by 3D FEA and correction coefficients of the 2D FEA models are derived. A new optimization process using 2D FEA models is then performed.  ...  Each intermediate optimal solution is evaluated by 3D FEA and correction coefficients of the 2D FEA models are derived. A new optimization process using 2D FEA models is then performed.  ...  The overall convergence of the process is very fast and the 3D FEA software always validates the optimal solution.  ... 
doi:10.1109/tdei.2015.005047 fatcat:jgykkr6xszfw3axsefs4vbsx44

Optimization for Sustainable Manufacturing - Application of Optimization Techniques to Foster Resource Efficiency

Enrico Ferrera, Riccardo Tisseur, Emanuel Lorenço, E. J. Silva, Antonio J. Baptista, Gonçalo Cardeal, Paulo Peças
2017 Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security  
models, enabling support and optimize sustainable decision making process and identification of potential improvements.  ...  Moreover, the optimisation techniques should centre the process through design/configuration of the production system, without considering time, in order not to limit the physical agents. 424  ...  ACKNOWLEDGEMENTS This work was supported by the European Union's Horizon 2020 research and innovation program through the MAESTRI project (grant n° 680570).  ... 
doi:10.5220/0006374604240430 dblp:conf/iotbd/FerreraTLSBCP17 fatcat:erh6kytsq5cjhhs2hdjzo5kbya

Interactive evolutionary computation in process engineering

Janos Madar, Janos Abonyi, Ferenc Szeifert
2005 Computers and Chemical Engineering  
In practical system identification, process optimization and controller design, it is often desirable to simultaneously handle several objectives and constraints.  ...  The results show that IEC is an efficient and comfortable method to incorporate the prior knowledge of the user into optimization problems.  ...  Acknowledgement The authors would like to acknowledge the support of the Cooperative Re-  ... 
doi:10.1016/j.compchemeng.2004.12.009 fatcat:jkeibkzmxbeknhs7dg7q6gumeq

Real-coded genetic algorithm for system identification and controller tuning

K. Valarmathi, D. Devaraj, T.K. Radhakrishnan
2009 Applied Mathematical Modelling  
In the proposed genetic algorithm, the optimization variables are represented as floating point numbers.  ...  This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants.  ...  It is observed that the variation of the fitness during the GA run for the best case and shows the generation of optimal variables.  ... 
doi:10.1016/j.apm.2008.11.006 fatcat:iaxqvwmns5ad5clkb3ei4cbvne

Design of IG-based Fuzzy Models Using Improved Space Search Algorithm
개선된 공간 탐색 알고리즘을 이용한 정보입자 기반 퍼지모델 설계

Sung-Kwun Oh, Hyun-Ki Kim
2011 Journal of Korean institute of intelligent systems  
The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification.  ...  The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models.  ...  The identification process is comprised of two phases, namely a structural optimization and parametric optimization.  ... 
doi:10.5391/jkiis.2011.21.6.686 fatcat:ehineioxjneu5mhgst7ggyawqy

Design of heterogeneous interior notched specimens for material mechanical characterization

Mariana Conde, António Andrade-Campos, Miguel Guimarães Oliveira, João Miguel Peixoto Martins
2021 ESAFORM 2021  
The optimization problem is driven by a cost function composed by several indicators of the heterogeneity present in the specimen.  ...  A state-of-the-art constitutive model generally involves a large number of parameters, and according to classical procedures, this requires many mechanical experiments for its accurate identification.  ...  The process starts by defining the design variables of the optimization procedure. For the definition of the specimen shape, different curve parameterization can be used.  ... 
doi:10.25518/esaform21.2502 fatcat:ucsibbwhmvhufhv7cethforavm

Automatic Tuning of Fuzzy Partitions in Inductive Reasoning [chapter]

Angela Nebot
2003 Lecture Notes in Computer Science  
The first step of FIR methodology is the fuzzification process that converts quantitative variables into fuzzy qualitative variables.  ...  In this process it is necessary to define the number of classes into which each variable is going to be discretized.  ...  The author thanks the suggestions of F.E. Cellier, F. Mugica and P. Villar during the development of this research.  ... 
doi:10.1007/978-3-540-24586-5_68 fatcat:agq7kqmn7zcztf2uvurlp5fbeq
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