Download NEURAL NETWORK

NEURAL NETWORK
By : Farideddin Behzad
Supervisor : Dr. Saffar Avval
May 2006
Amirkabir University of Technology
Agenda
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Definition
Application fields
History
Application
Biological inspiration
Mathematical model
Basic definition
Learning
Neuron types and some issues
Example of application in energy & engineering
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Definition
Haykin(1999)
 massive parallel-distributed processor
 natural propensity for storing experiential knowledge
 available for use.
 Acquiring knowledge by the network from its environment
through a learning process
 Using interneuron connection strengths, (a.k.a. synaptic
weights), to store the acquired knowledge
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Application fields
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Data analysis
Pattern recognition
Control application
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History
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1943, Warren McCulloch & Walter Pitts, works of neurons
1960, Bernard Widrow & Marcian Hoff, developed ADALINE
and MADLINE
From late 1960s to 1981, decreasing of researches
Early 1980s, renewed interest in neural network
1986, Daivid Rummelhart & James McLand, error backpropagation algorithm
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Applications
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Aerospace industry
Automotive industry
Banking
Military industry
Economics
Manufacturing
Medical applications
Oil & petroleum industry
And many more …
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Biological inspiration
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Brain structure
Cell
Dendrites
Cell
body
Axon
Denderites
Soma (cell body)
Axon
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Mathematical model
Node
x1
w1
x2
Inputs
w2
x3
…
w3
xn-1
.
xn
n
z   wi xi ; y  H ( z )
Output
y
i 1
wn-1
wn
Artificial neural cell
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Mathematical model
Mathematic model of artificial neural cell
p
w
n

f
  wp  b 
a
output
input
Cell body
b
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Basic definition
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Architecture: formal mathematical description of a
Neural Network. (feed-forward & feed-back)
Layer or Slab: A subset of neurons in a neural network.
(Input, Hidden, Output)
Episodical vs continuous networks
Neuron weight
Activation function
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Activation function
Linear
Activation function
Non-Linear
Step
Sigmoid
Linear
Gaussian
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Learning
Supervised learning
learning
Unsupervised learning
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Coincidence learning
Performance learning
Competitive learning
Filter learning
Spatiotemporal learning
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Neuron types
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Hebb
Perceptron
Adaline
Kohonen
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Some issues
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Training dataset
Test dataset
Network size
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Example of application in
energy
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Soleimani. M, Thomas. B, Per Fahlen, “Estimation operative
temperature of building using artificial neural network”, Journal
of Energy and Building 38 ,2006
Luis M. Romeo, Raquel Gareta, “ neural network for evaluating
boiler behaviour”, Applied Thermal Engineering 26, 2006
Seyedan B., Ching C.Y., “Sensitivity analysis of freestream
turbulence parameter on stagnation region heat transfer using a
neural network”, International Journal of Heat and Fluid Flow,
2006
Perez-roa P., Vesovic V., “Air-pollution modelling in an urban
area: Correlation turbulent diffusion coefficients by means of an
artifical neral network approach”, Atmospheric Environment 40,
2006
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References
،‫ انتشارات دانشگاه صنعتي اميركبير‬،“‫ ”مباني شبكه هاي عصبي‬،‫منهاج محمد باقر‬
81‫پاييز‬
2.
Hecht-Nielsen R., “Neurocomputing“,
publishing company, 1991
3.
MATLAB help documentation
.1
Addison-Wesley
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