CAPES/PROCAD Intelligent Forecast System Applied to the Identification of Parameters in Flexible Structures

Abstract

The Intelligent Forecast System (IFS) consists of three units: the main unit is a multilayer neural network1 the second unit is based on fuzzy logic, which is used to prepare the data1 and the third unit is based on a genetic algorithm and is used to determine the best set of data to be analyzed. The data were set up by using a strategy that classifies the terms of the series into parts with the same quantity of elements. Each part is called a window. The IFS was trained by using a group of windows, which is called a set of training windows. Next, the IFS was applied to solve several problems related to the evaluation of structural integrity. The IFS was applied as an identification strategy and forecast of parameters in a flexible structure. Additionally, by using the IFS we have developed a new strategy to analyze the temporary series obtained from vibrations of flexible structures with the distribution of variable mass. The results obtained in this research, using the IFS applied as an identification strategy and forecast of parameters in a flexible structure, show the effectiveness of the IFS.

RETRACTED: A critical review of structural system control by the large-scaled neural network linear-deferential-inclusion-based criterion

“…The Intelligent Forecast System (IFS) is made up of three units: the main unit is a multilayer neural network; the second unit is based on fuzzy logic, which is used to prepare the data; and the third unit is based on a genetic algorithm and is used to determine the best set of data to be analyzed (De Melo et al, 2005). The data are set up by using a strategy that classifies the terms of the series into parts with the same quantity of elements.…”

RETRACTED: Applications of linear differential inclusion-based criterion to a nonlinear chaotic system: a critical review

“…After modeling the whole structure, including the robotic modules and the joint parameters, simulations of the vibration control for the modular robot in several configurations were carried out. In the same year, De Melo et al (2005) developed an intelligent forecast system consisting of three units: a main unit comprised of a multilayer NN; a second unit based on fuzzy logic, which was used to prepare the data; and a third unit based on a genetic algorithm, used to determine the best set of data to be analyzed. The data were set up using a strategy that classifies the terms of the series into parts with the same quantity of elements.…”

RETRACTED: Applications of the fuzzy-neural Lyapunov criterion to multiple time-delay systems

“…For example, in the past 10 years alone, over 6000 papers concerning the fuzzy-neural topic have been published in the web of science. The intelligent forecast system (IFS) ( 2005) is composed of three units: the main unit is a multilayer neural network; the second unit is based on fuzzy logic, which is used to prepare the data; the third unit, which is based on a fuzzy algorithm, is used to decide the best set of data to be analyzed (De Melo et al, 2005). The data are set up, using a strategy that classifies the terms of the series into parts with the same quantity of elements.…”

“…The flexible structure of the IFS is applied as an identification strategy and forecast of parameters. In addition, by using the IFS, De et al (2005) developed a new strategy to analyze the temporary series acquired from the vibration of flexible structures with the distribution of variable mass. The results acquired using the IFS applied as an identification strategy and forecaster of parameters in a flexible structure display the effectiveness of the IFS.…”