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        Evolutionary computational approaches for data-driven modeling of multi-dimensional memory-dependent systems

        Ali Bolourchi,Sami F. Masri 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.3

        This study presents a novel approach based on advancements in Evolutionary Computation fordata-driven modeling of complex multi-dimensional memory-dependent systems. The investigated exampleis a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjectedto external excitations at three points. The proposed technique of this research adapts Genetic Programmingfor discovering the optimum structure of the differential equation of an auxiliary variable associated withevery specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all otherdegrees-of-freedom. After the termination of the first phase of the optimization process, a system ofdifferential equations is formed that represent the multi-dimensional hysteretic system. Then, the parametersof this system of differential equations are optimized in the second phase using Genetic Algorithms to yieldaccurate response estimates globally, because the separately obtained differential equations are coupledessentially, and their true performance can be assessed only when the entire system of coupled differentialequations is solved. The resultant model after the second phase of optimization is a low-orderlow-complexity surrogate computational model that represents the investigated three-dimensionalmemory-dependent system. Hence, this research presents a promising data-driven modeling technique forobtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonablyaccurate results, and can be generalized to many problems, in various fields, ranging from engineering toeconomics as well as biology

      • SCIESCOPUS

        Evolutionary computational approaches for data-driven modeling of multi-dimensional memory-dependent systems

        Bolourchi, Ali,Masri, Sami F. 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.3

        This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

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