Soft Computing
Colloquium 2
Selection of neural network,
Hybrid neural networks.
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2
Objectives
•
Why too much of models of neural
networks (NN)?
•
Classes of tasks and classes of NN
•
Hybrid neural networks
•
Hybrid model based on MLP and ART

2
•
Paths to improvement of neural networks
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3
Submit a questions to discuss
•
Paths to improvement of neural networks:
–
Development of growth neural networks with
feedback and delays
–
Development of theory of spiking neurons and
building of associative memory based on its
–
Development of neural network in which
during learning logical (verbal) inference
would appearance from associative memory
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4
Why too much of models of neural
networks (NN)?
Models of neural networks
simulate separate aspects
of working of brain (e.g.
associative memory,
but
how it works in whole is
unknown for us.
Questions:
1)
What is consciousness?
2)
What is role of emotions?
3)
How different areas of
brain are coordinated?
4) How associative links
are transformed and used in
logical inference and
calculations?
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Classes of tasks :
•
prediction
•
classification
•
data association
•
data conceptualization
•
data filtering
•
Neuromathematics
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Classes of Neural Networks:
•
Multi Layer Networks
–
Multi Layer Perceptron (MLP)
•
Supervised learning
–
Radial Basis Functions (RBF

networks)
•
Supervised learning
–
Recurrent Neural Networks
(Elman, Jordan)
•
Supervised learning
•
Reinforcement learning
–
Counterpropagation network
•
Supervised learning
•
One

layer networks
–
Self

organized map (MAP)
•
Unsupervised learning
–
Artificial resonance theory
(ART)
•
Unsupervised learning
–
Hamming network
•
Supervised learning
•
Fully interconnected networks
–
Hopfield network
•
Supervised learning
–
Boltzmann machine
•
Supervised learning
–
Bi

directional associative
memory
•
Supervised learning
•
Spiking networks
•
Supervised learning
•
Unsupervised learning
•
Reinforcement learning
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Counterpropagation network
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Network Selector Table
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Hybrid Neural Networks.
•
Includes:
–
Main neural network
–
Other neural network
•
Preprocessing
•
Postprocessing
•
Some models of neural networks consist of
some layers working by different manner and so
such neural networks may be viewed as hybrid
neural networks (including more elementary
networks)
•
Some authors calls hybrid neural networks such
model which combine paradigms of neural
networks and knowledge engineering.
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11
Hybrid Neural Network based on models of
Multi

Layer Perceptron and Adaptive
Resonance Theory (A.Gavrilov, 2005)
•
Aims to keep capabilities of ARM
(plasticity and stability)
•
Include in ART capabilities of MLP during
learning to obtain complex secondary
features from primary features (to
approximate any function)
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Disadvantages of model ART

2 for recognition of images
•
It uses of metrics of primary features of
images to recognize of class or create of
new class,
•
Transformations of graphic images (shift
or rotation or others) essentially influence
on distance between input vectors
•
So it is unsuitable for control system of a
mobile robots
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Architecture of hybrid neural network
output vector
output layer:
clusters
input layer:
input variables
y
1
y
2
y
m
input layer of
ART

2, output
layer of perceptron
hidden layer of
perceptron
input vector
x
1
x
2
x
n
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Algorithm of learning without
teacher
•
Set of initial weights of neurons; N
out
:=0;
•
Input of image

example and calculate of outputs of
perceptron;
•
If N
out
=0 then forming of new cluster

output neuron;
•
If N
out
>0 then calculate of distances between
weight vector of ART

2 and output vector of
perceptron, select of minimum of them (selection of
output neuron

winner) and decide to create or not
new cluster;
•
If new cluster is not created then calculate new
values of weights of output neuron

winner and
calculate new weights of perceptron with algorithm
“error back propagation”.
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The illustration of algorithm
1
3
4
2
5
R
1
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16
Images and parameters used in
experiments
Quantity of input neurons (pixels)

10000 (100х100),
Quantity of neurons in hidden layer of perceptron

20,
Quantity of output neurons of perceptron (in input layer of ART

2)
Nout

10,
Radius of cluster
R
was used in experiments in different manners:
1) adapt and fix,
2) calculate for every image by formulas
S/(2N
out
)
,
where S
–
average input signal,
N
out
–
number of output neurons of perceptron,
3) calculated as 2D
min
,
where D
min
–
minimal distance between input vector of ART2 and weight
vectors in previous image.
Activation function of neurons of perceptron is rational sigmoid with parameter a=1,
Value of learning step of perceptron is 1,
Number of iterations of recalculation of weights of perceptron is from 1 to 10.
1)
2)
3)
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Series of images 1

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Program for experiments
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For sequence of images of series 1, 2, 1, 2 (a dark
points are corresponding to 2nd kind of calculation
of vigilance and light
–
to 1st one).
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For sequence of images of series 1 at different
number of iteration of EBP algorithm: 1, 3, 5, 7, 9.
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21
Paths to improvement of neural networks
•
Development of growth neural networks
with feedback and delays
•
Development of theory of spiking neural
networks and building of associative
memory based on them
•
Development of neural network in which
during learning logical (verbal) inference
would appearance from associative
memory
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