While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In ""Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior"", authors David Scarborough and Mark Somers bring researchers, academics, and practitioners up to speed on this emerging field, in which powerful computing capabilities offer new insights into longstanding, complex I/O questions such as employee selection and behavioral prediction. Neural networks mimic the way the human brain works, using interconnected nodes and feedback loops to ""learn"" to recognize even subtle patterns in vast amounts of data. They can process data far more quickly and efficiently than conventional techniques can, and produce better empirical results. They are especially useful for modeling nonlinear processes. The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems. Academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.
While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In ""Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior"", authors David Scarborough and Mark Somers bring researchers, academics, and practitioners up to speed on this emerging field, in which powerful computing capabilities offer new insights into longstanding, complex I/O questions such as employee selection and behavioral prediction. Neural networks mimic the way the human brain works, using interconnected nodes and feedback loops to ""learn"" to recognize even subtle patterns in vast amounts of data. They can process data far more quickly and efficiently than conventional techniques can, and produce better empirical results. They are especially useful for modeling nonlinear processes. The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems. Academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.
Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots ...
The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and...
Micromechanical manufacturing based on microequipment creates new possibi- ties in goods production. If microequipment sizes are comparable to the sizes of the microdevices to be produced, it is...
The Structure of Neural Networks.- Transfer from the Logical Basis of Boolean Elements "AND, OR, NOT" to the Threshold Logical Basis.- Qualitative Characteristics of Neural Network Architectures.-...
Neural networks are one of the fast-growing paradigms for learning systems with a wide variety of potential applications in industry. In particular there are general results which prove the universal...
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