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VisSim/Neural-Net
excels at nonlinear system identification, problem diagnosis, decision
making, prediction, and other problems where pattern recognition is
important and precise computational answers are not readily available.
Within the engineering community, scientists are using neural networks
for learning nonlinear dynamic behavior from historic data sets. Once
trained, neural networks are used to predict plant behavior based on
input values.
Neural networks can be both trained and used
for prediction directly from a VisSim diagram.
Features
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Up to 32
layers per network
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Up to 32767
neurons per layer
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up to 128
neural networks per diagram
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Five learning methods
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User-selectable
learn rate
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User-selectable
neighborhoods
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User-specified
smoothing factor
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Table-driven
weight initialization
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get Started
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