@InProceedings{AnochiCampShigLuz:2014:DaAsAr,
               author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio Hideiti and Luz, Eduardo F. P. da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)}",
                title = "Data assimilation with arti cial neural networks self-con guring 
                         by MPCA",
            booktitle = "Abstracts...",
                 year = "2014",
         organization = "EngOpt.",
             abstract = "Artificial Neural Networks (ANN) are computational techniques that 
                         present a mathematical model inspired by the neural structure of 
                         biological organisms, acquiring knowledge through experience, 
                         which have been a technique successfully employed in many 
                         applications on several research fields and currently under 
                         intensive research worldwide. ANN with learning supervised have 
                         emerged as excellent tools for deriving data oriented models, due 
                         to their inherent characteristic of plasticity that permits the 
                         adaptation of the learning task when data is provided. In addition 
                         to plasticity, ANN also present generalization and fault tolerance 
                         characteristics that are fundamental for systems that depend on 
                         observational. Although much has been studied, there are still 
                         many questions about the ANN models that need to be addressed. One 
                         of the main issues of research in supervised ANN is to search for 
                         an architecture optimum. In this paper, the determination of 
                         optimal parameters for the neural network is formulated as an 
                         optimization problem, solved with the use of meta-heuristic 
                         Multiple Particle Collision Algorithm (MPCA). The MPCA 
                         optimization algorithm emulates a collision process of multiple 
                         particles greatly inspired on two physical behaviour inside of a 
                         nuclear reactor absorption and scattering. The cost function has 
                         two terms: a square difference between ANN output and the target 
                         data (for two data set: learning process, and the generalization, 
                         and a penalty term used to evaluate the complexity for the new 
                         network architecture at each iteration. The concept of network 
                         complexity is associated to the number of neurons and the number 
                         of iterations in the training phase. In this work, two types of 
                         neural networks are used, the radial basis function network (RBF) 
                         and recurrent Elman. Here, the self-configuring networks are 
                         applied to perform data assimilation to emulate the Kalman filter 
                         is carried out with linear 1D wave equation.",
  conference-location = "Lisbon",
      conference-year = "2014",
                label = "lattes: 2720072834057575 1 AnochiCampShigLuz:2014:DaAsAr",
             language = "en",
           targetfile = "Anochi_data.pdf",
                  url = "http://www.dem.ist.utl.pt/engopt2014/",
        urlaccessdate = "12 maio 2024"
}