Artificial neural network research is one of the new directions for
new generation computers. Current research suggests that open box
artificial higher order neural networks (HONNs) play an important
role in this new direction. HONNs will challenge traditional
artificial neural network products and change the research
methodology that people are currently using in control and
recognition areas for the control signal generating, pattern
recognition, nonlinear recognition, classification, and prediction.
Since HONNs are open box models, they can be easily accepted and
used by individuals working in information science, information
technology, management, economics, and business fields. Emerging
Capabilities and Applications of Artificial Higher Order Neural
Networks contains innovative research on how to use HONNs in
control and recognition areas and explains why HONNs can
approximate any nonlinear data to any degree of accuracy, their
ease of use, and how they can have better nonlinear data
recognition accuracy than SAS nonlinear procedures. Featuring
coverage on a broad range of topics such as nonlinear regression,
pattern recognition, and data prediction, this book is ideally
designed for data analysists, IT specialists, engineers,
researchers, academics, students, and professionals working in the
fields of economics, business, modeling, simulation, control,
recognition, computer science, and engineering research.
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