The Experts below are selected from a list of 483684 Experts worldwide ranked by ideXlab platform
I D Mayergoyz - One of the best experts on this subject based on the ideXlab platform.
-
Identification of vector preisach models from arbitrary measured data using neural networks
Journal of Applied Physics, 2000Co-Authors: A A Adly, Salwa K Abdelhafiz, I D MayergoyzAbstract:It has been recently shown that the Identification Process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the Identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the Identification Process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
-
a new vector preisach type model of hysteresis
Journal of Applied Physics, 1993Co-Authors: A A Adly, I D MayergoyzAbstract:New isotropic vector Preisach‐type models of hysteresis are introduced. These new models contain additional unknown functions. This presents the opportunity to incorporate more experimental data in the Identification Process than in the case of previously suggested vector Preisach models, thus leading to more accurate models. The solution of the Identification problem is discussed and some results of experimental testing of these models are reported.
A A Adly - One of the best experts on this subject based on the ideXlab platform.
-
Identification of vector preisach models from arbitrary measured data using neural networks
Journal of Applied Physics, 2000Co-Authors: A A Adly, Salwa K Abdelhafiz, I D MayergoyzAbstract:It has been recently shown that the Identification Process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the Identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the Identification Process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
-
a new vector preisach type model of hysteresis
Journal of Applied Physics, 1993Co-Authors: A A Adly, I D MayergoyzAbstract:New isotropic vector Preisach‐type models of hysteresis are introduced. These new models contain additional unknown functions. This presents the opportunity to incorporate more experimental data in the Identification Process than in the case of previously suggested vector Preisach models, thus leading to more accurate models. The solution of the Identification problem is discussed and some results of experimental testing of these models are reported.
Connie Marie Gaglio - One of the best experts on this subject based on the ideXlab platform.
-
the role of mental simulations and counterfactual thinking in the opportunity Identification Process
Entrepreneurship Theory and Practice, 2004Co-Authors: Connie Marie GaglioAbstract:The Process of identifying, shaping, and pursuing market opportunities is emerging as a focal point in the field of entrepreneurship. Scholarly efforts to date have considered what happens during this Process; it is time to turn attention to how and why. This article examines one such “how” question: how do entrepreneurs think and reason such that they identify innovative opportunities? Specifically, the cognitive Processes of mental simulation and counterfactual thinking are proposed as mechanisms by which entrepreneurs identify and develop innovative opportunities. Propositions regarding the application of these cognitive Processes to opportunity Identification are presented and discussed.
-
the psychological basis of opportunity Identification entrepreneurial alertness
Small Business Economics, 2001Co-Authors: Connie Marie Gaglio, Jerome A KatzAbstract:Opportunity Identification represents a unique entrepreneurial behavior yet its Processes and dynamics remain mysterious. Entrepreneurial alertness, a distinctive set of perceptual and information-Processing skills, has been advanced as the cognitive engine driving the opportunity Identification Process. To date, empirical support has been equivocal; however, these early studies suffer from fundamental mistakes in theory and method. These mistakes are examined and addressed. A research agenda for the systematic and conceptually sound study of entrepreneurial alertness and opportunity Identification is outlined.
Dennis J Aigner - One of the best experts on this subject based on the ideXlab platform.
-
opportunity discovery and creation in social entrepreneurship an exploratory study in mexico
Journal of Business Research, 2017Co-Authors: Monica Gonzalez, Bryan W Husted, Dennis J AignerAbstract:Abstract Building on the literature on opportunity Identification among commercial entrepreneurs, we propose a conceptual framework for the opportunity Identification Process in social entrepreneurship that includes both opportunity discovery and opportunity creation. We develop scales to measure these constructs and examine their validity and reliability. In addition, we develop and test hypotheses explaining the causes of opportunity discovery and opportunity creation. Our results confirm that opportunity discovery and opportunity creation are mutually exclusive constructs. We also find support for the role of radical innovation and information search in the opportunity Identification Process in a sample of Mexican social entrepreneurs.
Salwa K Abdelhafiz - One of the best experts on this subject based on the ideXlab platform.
-
Identification of vector preisach models from arbitrary measured data using neural networks
Journal of Applied Physics, 2000Co-Authors: A A Adly, Salwa K Abdelhafiz, I D MayergoyzAbstract:It has been recently shown that the Identification Process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the Identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the Identification Process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
