ARTIFICIAL NEURAL NETOWRKSNEURONCLASSIFICATION OF NEURAL NETWORK
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Ar t if icial Neural Net works for process cont rol
Puneet Kr Singh
Mtech ( FT)
1
st
Y r
P K Singh, F O E, D E I
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What is a Neural Network?
•Biologically motivated approach to
machine learning
Modern digital computers outperform human in the
domain of numeric computation & related symbol
manipulation
However humans can effortlessly solve complex
perceptual problems….
like Recognizing a man in a crowd from a mere
glimpse of his face at such a high speed & extent as to
dwarf the world’s fastest computers
P K Singh, F O E, D E I
ELECTRON MICROGRAPH OF A REAL NEURON
P K Singh, F O E, D E I
NN as an model of
br ai n-l i ke Comput er
An ar t i f i ci al neur al net w or k (ANN) i s a
massi vel y par al l el di st r i but ed pr ocessor t hat
has a nat ural propensi t y for st or i ng
exper i ment al know l edge and maki ng i t
avai l abl e f or use. It means t hat :
Know l edge i s acqui r ed by t he net w or k
t hr ough a l ear ni ng (t r ai ni ng) pr ocess;
ANN as a Brain-Like Computer
t hr ough a l ear ni ng (t r ai ni ng) pr ocess;
The st r engt h of t he i nt er connect i ons
bet w een neur ons i s i mpl ement ed by means
of t he synapt i c w ei ght s used t o st or e t he
know l edge.
The l ear ni ng process i s a pr ocedur e of t he
adapt i ng t he w ei ght s w i t h a l ear ni ng
al gor i t hm i n or der t o capt ure t he know l edge.
On mor e mat hemat i cal l y, t he ai m of t he
l ear ni ng process i s t o map a gi ven rel at i on
bet w een i nput s and out put (out put s) of t he
net w or k.
Br ai n
The human br ai n i s st i l l not w el l
under st ood and i ndeed i t s
behavi or i s ver y compl ex!
Ther e ar e about 10 bi l l i on
neur ons i n t he human cor t ex and
60 t r i l l i on synapses of connect i ons
The br ai n i s a hi ghl y compl ex,
nonl i near and par al l el comput er
(i nf or mat i on-pr ocessi ng syst em)
P K Singh, F O E, D E I
P K Singh, F O E, D E I
A Neuron
1
x
n
x
1
( ,..., )
n
x f x
.
.
.
φ(z)
0 1 1
...
n n
z w wx w x = + + +
1 0 1 1
( ,..., ) ( ... )
n n n
f x x F w w x w x = + + +
Where f is a function to be earned.
are the inputs.
φ is the activation function.
n
x
0 1 1
...
n n
z w wx w x = + + +
1
,...,
n
x x
Z i s t he w ei ght ed sum
P K Singh, F O E, D E I
( )
z z =
Li near act i vat i on Logi st i c act i vat i on
( )
1
1
z
z
e
÷
=
+
z
z
1
0
Σ
Ar t if icial Neuron:
Classical Act ivat ion Funct ions
Thr eshol d act i vat i on
Hyper bol i c t angent act i vat i on
( ) ( )
u
u
e
e
u tanh u
2
2
1
1
÷
÷
+
÷
= =
( )
1, 0,
sign( )
1, 0.
if z
z z
if z
> ¦
= =
´
÷ <
¹
z
z
z
-1
1
0
0
Σ
1
-1
P K Singh, F O E, D E I
Neural Net work
Neural Network learns by adjusting the weights so as to be able to
correctly classify the training data and hence, after testing phase,
to classify unknown data.
Neural Network needs long time for training. Neural Network needs long time for training.
Neural Network has a high tolerance to noisy and incomplete data
P K Singh, F O E, D E I
Learning
The procedure that consists in estimating the parameters of neurons (setting up
the weights) so that the whole network can perform a specific task.
2 types of learning
Supervised learning
Unsupervised learning
Supervised learning which incorporates an external teacher, so that each output Supervised learning which incorporates an external teacher, so that each output
unit is told what its desired response to input signals ought to be.
Unsupervised learning uses no external teacher and is based upon only local
information. It is also referred to as self-organization, in the sense that it self-
organizes data presented to the network and detects their emergent collective
properties.
P K Singh, F O E, D E I
Threshold Neuron (Percept ron)
• Output of a threshold neuron is binary, while inputs may be either
binary or continuous
• If inputs are binary, a threshold neuron implements a Boolean
function
• The Boolean alphabet {1, -1} is usually used in neural networks • The Boolean alphabet {1, -1} is usually used in neural networks
theory instead of {0, 1}.
• Correspondence with the classical Boolean alphabet {0, 1} is
established as follows:
1 2 ( 0 1 {0 1) {1 1} } 1 1 1
y
; ; , x= - y - ,- y , x e = e ¬ ÷ ÷ ÷
P K Singh, F O E, D E I
Threshold Boolean Funct ions: Geomet rical
Int erpret at ion
“ OR” (Di sjunct ion) is an example of t he
t hreshold (linearly separable) Boolean f unct ion:
“ -1s” are separat ed f rom “ 1” by a line
XOR is an example of t he non-t hreshold (not li nearly
separable) Boolean f unct ion: it is impossible
separat e “ 1s” f rom “ -1s” by any singl e li ne
(-1, 1) (1, 1)
=
1
1
2
1
1
n
y
y
y
i
y
2. M at hemat i cal model of quant i zat i on:
“ Lear ni ng by Exampl es”
P K Singh, F O E, D E I
Applicat ion of Ar t if icial Neural Net work in Fault
Det ect ion St udy of Bat ch Est erif icat ion Process
The complexity of most chemical industry always tends to create a problem in
monitoring and supervision system.
Prompt fault detection and diagnosis is a best way to handle and tackle this problem.
There are different methods tackling different angle. One of the popular methods is
artificial neural network which is a powerful tool in fault detection system.
In this, a production of ethyl acetate by a reaction of acetic acid and ethanol in a
batch reactor is applied. batch reactor is applied.
The neural network with normal and faulty event is executed on the data collected
from the experiment.
The relationship between normal-faulty events is captured by training network
topology.
The ability of neural network to detect any process faults is based on their ability to
learn from example and requiring little knowledge about the system structure.
P K Singh, F O E, D E I
CONCLUSION Fault diagnosis for pilot-plant batch esterification process is
investigated in this work by a feed forward neural model by implementing multilayer
perceptron. The effect of catalyst concentration and catalyst volume are studied and
classified successfully using the neural process model. The results displayed that
neural network is able to detect and isolate two fault studies with a nice pattern
classification. P K Singh, F O E, D E I
Temperat ure cont rol in ferment ers: applicat ion of
neural net s and feedback cont rol in breweries
The main objective of on-line quality control in fermentation is to perform the production
processes as reproducible as possible.
Since temperature is the main control parameter in the fermentation process of beer
breweries, it is of primary interest to keep it close to the predefined set point. Here, we
report on a model-supported temperature controller for large production-scale beer
fermenters.
The dynamic response of the temperature in the tank on temperature changes in the cooling The dynamic response of the temperature in the tank on temperature changes in the cooling
elements has been modeled by means of a difference equation.
The heat production within the tank Is taken into account by means of a model for the
substrate degradation.
Any optimization requires a model to predict the consequences of actions. Instead of using a
conventional mathematical model of the fermentation kinetics, an artificial neural network
approach has been used.
The set point profiles for the temperature control have been dynamically optimized in order
to minimize the production cost while meeting the constraints posed by the product quality
requirements.
P K Singh, F O E, D E I
P K Singh, F O E, D E I
P K Singh, F O E, D E I
P K Singh, F O E, D E I
P K Singh, F O E, D E I
P K Singh, F O E, D E I
Ar t i f i ci al
Int elligent
Cont rol
s
Technical
Diagnist ic
s
Int elligent
Dat a Analysis
and Signal
Advance
Robot ics
Machine
Vision
Applicat ions of Ar t if icial Neural Net works
Ar t i f i ci al
Int el l ect w i t h
Neur al
Net w or ks
and Signal
Processing
Vision
Image &
Pat t ern
Recognit ion
Int elligent
Securit y
Syst ems
Devices
Int elligent
l
Medicine
Devices
Int elligent
Expert
Syst ems
P K Singh, F O E, D E I
Applicat ions: Classif icat ion
Business
• Credit rat ing and risk assessment
• I nsurance risk evaluat ion
• Fraud det ect ion
• I nsider dealing det ect ion
• Market ing analysis
• Signat ure verificat ion
• I nvent ory cont rol
Security
• Face recognit ion
• Speaker verificat ion
• Fingerprint analysis
Medicine
• I nvent ory cont rol
Engineering
• Machinery defect diagnosis
• Signal processing
• Charact er recognit ion
• Process supervision
• Process fault analysis
• Speech recognit ion
• Machine vision
• Speech recognit ion
• Radar signal classificat ion
Medicine
• General diagnosis
• Det ect ion of heart defect s
Science
• Recognising genes
• Bot anical classificat ion
• Bact eria ident ificat ion
P K Singh, F O E, D E I
Applicat ions: Modeling
Business
• Predict ion of share and commodit y prices
• Predict ion of economic indicat ors
• I nsider dealing det ect ion
• Market ing analysis
• Signat ure verificat ion
• I nvent ory cont rol
Science
Engineering
• Transducer linerisat ion
• Colour discriminat ion
• Robot cont rol and navigat ion
• Process cont rol
• Aircraft landing cont rol
• Car act ive suspension cont rol
• Print ed Circuit aut o rout ing
• I nt egrat ed circuit layout
• I mage compression
Science
• Predict ion of t he performance of
drugs from t he molecular st ruct ure
• Weat her predict ion
• Sunspot predict ion
Medicine
• . Medical imaging
and image processing
P K Singh, F O E, D E I
Applicat ions: Forecast ing
• Fut ure sales
• Product ion Requirement s
• Market Performance
• Economic I ndicat ors
• Energy Requirement s • Energy Requirement s
• Time Based Variables
P K Singh, F O E, D E I
Applicat ions: Novelt y Detect ion
• Fault Monit oring
• Performance Monit oring
• Fraud Det ect ion
• Det ect ing Rat e Feat ures
• Different Cases
P K Singh, F O E, D E I
Thank you
For any suggestion …..
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P K Singh, F O E, D E I