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Multilayer Perceptron and Fuzzy Systems
C code Generator (1992)
[Deep Learning on Embedded Devices]
NeurFuzz™ 2.0 was released
in 1992 as a Multilayer Perceptron type neural
network training tool with generation of trained C source code usable for
embedded solutions. The application was developed to run in a DOS environment
and allowed you to train Multilayer Perceptron neural
networks with 500 inputs, 500 outputs and up to 20 hidden layers. Therefore
this application allowed the creation of Deep Learning solutions in a DOS
environment for embedded applications.
Neurfuzz™ was released in a
stripped down version 1.0 as freeware, with the number of hidden layers limited
to 2 , only 100 input/output and no DSP coprocessors
support.
NeurFuzz™ 2.0 allowed you
to train a Multilayer Perceptron neural network by
generating the source code in C language with all the trained synaptic weights
included in a header file.
Among the advanced features of the tool were:
1) Genetic Algorithm Training that could start automatically when
the Error Back Propagation-based gradient descent algorithm entered a local
minimum.
2) Simulated Annealing technology that prevented entry into local minima
during gradient descent.
3) Automatic correction of Epsilon and Momentum when learning with
Error Back Propagation.
4) Generation of Input and Output interfaces based on Fuzzy Logic
with trigonometric membership functions (or conventional triangular/trapezoidal
functions).
5) Generation of trained C code for applications on any HW
platform and any operating system.
6) Support for the most popular ISA PC cards based on DSP (Digital
Signal Processing) for acceleration of the learning process with Error Back
Propagation.
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