NeuroSolutions can be applied to a wide variety of problems. A few of our customers have
submitted summaries of their work to give others a chance
to see how NeuroSolutions can be used to apply neural
network technology to real-world applications.
Below are a few application summaries
for your review. If you would like to see more summaries
that pertain to your particular application, please
choose your application category from the menu at the
left.
Adaptive
Inverse Control of a Ventilator
NeuroDimension was awarded a research project from the
National Science Foundation SBIR program (DMI-9861425).
The goal of this project was to research and develop an
adaptive inverse control architecture to control a
ventilator. In this project we developed some very
interesting technology, both in terms of a NeuroSolutions development environment and in terms of adaptive inverse
control.
Cost Management
The construction industry has been consistently
criticized for poor performance in attaining its clients
requirements. Time and cost overruns are very common and
accepted as an inevitable part of construction. These
overruns are a major cause of disruption, delay, disputes
and excess cost. Yet no empirical method or tool,
quantitative or otherwise, is available for managing or
controlling them. The conventional approach is to include
a percentage of the project cost as contingency in the
pre-contract budget for their occurrences. The allocated
contingency based on this method is largely judgmental
and arbitrarily allocated, which is often overly
simplistic and unrealistic. NeuroSolutions is used to
implement a three-layer MLP neural network model to
better predict the total contingency cost allowance for
variations on a given construction project.
Customer: Prof. A.O. Akinsola, University of
Wolverhampton
Quality Control
Ash problems in coal-fired power
plants result in decreases in efficiency, unscheduled
outages, equipment failures, and cleaning. Assessing the
potential impact of ash on power plant performance is
extremely complex and difficult due to coal variability,
the complexity of the ash behavior processes involved,
and changing operating conditions. To predict the impact
of ash on power plant performance, the impurities and
mineral contents of coal have to be determined. Current
coal quality evaluation methods are either inefficient or
very expensive and time consuming. NeuroSolutions was
used to develop a neural network that could quickly
determine the impurities and ash forming species in coal.
The results are compared with those from
computer-controlled scanning electron microscopy (CCSEM)
methods. The developed model shows promise and has the
potential to save coal-fired utilities millions of
dollars in dealing with various coal ash problems.
Customer: Prof. H. Salehfar, University of North
Dakota
More real-world applications can be
viewed by clicking the topics on the left.
Existing Customers:
Additional submissions
are welcome!
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