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Processing information and analyzing data efficiently and
effectively is crucial for any company that wishes to stay
competitive in its respective market. Nonlinear data presents new
challenges to organizations, however, due to its complexity and
unpredictability. The only technology that can properly handle this
form of data is artificial neural networks. These modeling systems
present a high level of benefits in analyzing complex data in a
proficient manner, yet considerable research on the specific
applications of these intelligent components is significantly
deficient. Applications of Artificial Neural Networks for Nonlinear
Data is a collection of innovative research on the contemporary
nature of artificial neural networks and their specific
implementations within data analysis. While highlighting topics
including propagation functions, optimization techniques, and
learning methodologies, this book is ideally designed for
researchers, statisticians, academicians, developers, scientists,
practitioners, students, and educators seeking current research on
the use of artificial neural networks in diagnosing and solving
nonparametric problems.
Processing information and analyzing data efficiently and
effectively is crucial for any company that wishes to stay
competitive in its respective market. Nonlinear data presents new
challenges to organizations, however, due to its complexity and
unpredictability. The only technology that can properly handle this
form of data is artificial neural networks. These modeling systems
present a high level of benefits in analyzing complex data in a
proficient manner, yet considerable research on the specific
applications of these intelligent components is significantly
deficient. Applications of Artificial Neural Networks for Nonlinear
Data is a collection of innovative research on the contemporary
nature of artificial neural networks and their specific
implementations within data analysis. While highlighting topics
including propagation functions, optimization techniques, and
learning methodologies, this book is ideally designed for
researchers, statisticians, academicians, developers, scientists,
practitioners, students, and educators seeking current research on
the use of artificial neural networks in diagnosing and solving
nonparametric problems.
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