Neural Networks - 123seminarsonly by hcj

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									NEURAL NETWORKS
Harshita Gupta (11508013)
Bioprocess Engineering
   What are ANN’s?
   Conventional computers VS ANNs
   How do they work?
   Why them?
   Applications
What Are Artificial Neural Network?

   An Artificial Neural Network (ANN) is an information
    processing system
   It is composed of a large number of highly interconnected
    processing elements (neurons) working in unison to solve
    specific problems.
    ANNs, like people, learn by example, each configured for
    a specific application
   Learning in biological systems involves adjustments to the
    synaptic connections that exist between the neurons. This is
    true of ANNs as well.
   Modern neural networks are non-linear statistical data
    modeling tools. They are usually used to model complex
    relationships between inputs and outputs or to find patterns
    in data.
ANNs Vs Computers
   Neural networks process        Conventional computers
    information in a similar        use an algorithmic
    way the human brain             approach
    does, composed of a
    large number of highly
    interconnected processing
    units
   Neural networks learn by       The problem solving
    example which must be           capability of conventional
    selected very carefully.        computers is restricted to
    They cannot be                  problems that we already
    programmed to perform a         understand and know how
    specific task.                  to solve.
   They cannot be programmed            Conventional computers use a
    to perform a specific task. The       cognitive approach to
    examples must be selected             problem solving; the way the
    carefully otherwise useful time       problem is to solved must be
    is wasted or even worse the           known and stated in small
    network might be functioning          unambiguous instructions.
    incorrectly.                         These machines are totally
   The disadvantage is that              predictable; if anything goes
    because the network finds out         wrong is due to a software or
    how to solve the problem by           hardware fault.
    itself, its operation can be
    unpredictable
A Human Neurons
As we are all familiar this is how a conventional neuron looks
                                                                              Dendrites
                                                                                    Cell Body

                                                                             Threshold


                                                                                  axon

                                                                               Summation




A Network Neuron
In an artificial neural network, simple artificial nodes, variously called "neurons", "neurodes",
"processing elements" (PEs) or "units", are connected together to form a network of nodes mimicking
the biological neural networks — hence the term "artificial neural network".
An Improved Neurod
        W1

                                  f(x)=K(iwigi(x))
        W2



                 A more sophisticated neuron (figure 2) is
                 the McCulloch and Pitts model (MCP).
                 Here inputs are 'weighted', the effect
        Wn       that each input has at decision making is
                 dependent on the weight of the
                 particular input. In mathematical terms,
                 the neuron fires if and only if;
                 X1W1 + X2W2 + X3W3 + ... > T
    Network Model
   The most common type of
    artificial neural network consists
    of three groups, or layers, of
    units:
    a layer of "input" units is
    connected to The activity of the
    input units represents the raw
    information that is fed into the
    network.
    a layer of "hidden" units, The
    activity of each hidden unit is
    determined by the activities of
    the input units and the weights on
    the connections between the input
    and the hidden units.
   a layer of "output" units. The
    behavior of the output units
    depends on the activity of the
    hidden units.
Network Architectures

Feed-forward networks             Feedback networks

   Feed-forward ANNs allow          Feedback networks can have
    signals to travel one way         signals travelling in both
    only; from input to output.
    The output of any layer           directions by introducing
    does not affect that same         loops in the network.
    layer.




                                                               output
                                      input



                                                Hidden
    Feed-forward ANNs              Feedback networks are
    tend to be straight              dynamic; their 'state' is
    forward networks that            changing continuously
    associate inputs with            until they reach an
    outputs.                         equilibrium point. They
   This type of                     remain at the
    organization is also             equilibrium point until


                             input
                                     the input changes and a




                                                                 output
    referred to as bottom-
    up or top-down.                  new equilibrium needs
                                     to be found.
                                    Also known as
                                     interactive or recurrent
                                             Hidden
Firing rules
   The firing rule is an important concept in neural
    networks and accounts for their high flexibility.
    A firing rule determines how one calculates
    whether a neuron should fire for any input pattern.
    It relates to all the input patterns, not only the ones
    on which the node was trained.
   Take a collection of training patterns for a node, some
    inputs in which
    Cause it to fire -1
   Prevent it from firing- 0
   Then the patterns not in the collection??
      Here the node fires when in comparison , they have more
      input elements in common with the 'nearest' pattern in the 1-
      taught set than with the 'nearest' pattern in the 0-taught set.
     If there is a tie, then the pattern remains in the undefined
      state.
For Example


   For example, a 3-input neuron is taught As follows
   Output 1 when input (X1,X2 and X3) -111, 101
   Output 0 when the input is 000 or 001. ;
111, 101=1             000 or 001=0

Before Generalization
X1     0     0   0       0     1      1   1     1
X2     0     0   1       1     0      0   1     1

X3     0     1   0       1     0      1   0     1

Out    0     0   0/1     0/1   0/1    1   0/1   1


After Generalization
 X1    0     0   0       0      1     1   1     1
 X2    0     0   1       1      0     0   1     1

 X3    0     1   0       1      0     1   0     1

 Out   0     0   0       0/1    0/1   1   1     1
Pattern Recognition
                 X11
                 X12
                       TAN1
                 X13           F1

                X21
                X22     TAN2
                X23            F2

                 X31
                 X32           F3
                        TAN3
                 X33
X11   0   0     0   0     1     1   1     1
X12   0   0     1   1     0     0   1     1
                                              Top Neuron
X13   0   1     0   1     0     1   0     1
Out   0   0     1   1     0     0   1     1

X21   0   0     0   0     1     1   1     1
X22   0   0     1   1     0     0   1     1   Middle Neuron
X23   0   1     0   1     0     1   0     1
Out   1   0/1   1   0/1   0/1   0   0/1   0

X31   0   0     0   0     1     1   1     1
X32   0   0     1   1     0     0   1     1
                                              Bottom Neuron
X33   0   1     0   1     0     1   0     1
Out   0   0     1   1     0     0   1     0
Learning methods
Based on The memorization of patterns
and the subsequent response of the network

   associative mapping in which the network learns to produce a particular pattern
    on the set of input units whenever another particular pattern is applied on the set of
    input units. The associative mapping can generally be broken down into two
    mechanisms:
       auto-association: an input pattern is associated with itself and the states of input and
        output units coincide. This is used to provide pattern competition, i.e. to produce a pattern
        whenever a portion of it or a distorted pattern is presented. In the second case, the
        network actually stores pairs of patterns building an association between two sets of
        patterns.
       hetero-association: is related to two recall mechanisms:
           nearest-neighbor recall, where the output pattern produced corresponds to the input pattern stored,
            which is closest to the pattern presented, and
           interpolative recall, where the output pattern is a similarity dependent interpolation of the patterns
            stored corresponding to the pattern presented. Yet another paradigm, which is a variant associative
            mapping is classification, i.e. when there is a fixed set of categories into which the input patterns
            are to be classified.
   regularity detection in which units learn to respond to particular properties of the
    input patterns. Whereas in associative mapping the network stores the relationships
    among patterns, in regularity detection the response of each unit has a particular
    'meaning'. This type of learning mechanism is essential for feature discovery and
    knowledge representation
Conclusion
The computing world has a lot to gain from neural networks. Their ability to
learn by example makes them very flexible and powerful. Furthermore
there is no need to devise an algorithm (or understand inner mechanism) in
order to perform a specific task; They are also very well suited for real time
systems because of their fast response and computational times which are
due to their parallel architecture.
Neural networks also contribute to other areas of research such as
neurology and psychology. They are regularly used to model parts of living
organisms and to investigate the internal mechanisms of the brain.
Perhaps the most exciting aspect of neural networks is the possibility that
some day 'conscious' networks might be produced. There is a number of
scientists arguing that consciousness is a 'mechanical' property and that
'conscious' neural networks are a realistic possibility.
Finally, I would like to state that even though neural networks have a huge
potential we will only get the best of them when they are integrated with
computing, AI, fuzzy logic and related subjects.
    References
   An introduction to neural computing. Alexander, I. and Morton, H. 2nd edition
   http://media.wiley.com/product_data/excerpt/19/04713491/0471349119.pdf
   Neural Networks at Pacific Northwest National Laboratory
    http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
   Industrial Applications of Neural Networks (research reports Esprit, I.F.Croall, J.P.Mason)
   A Novel Approach to Modeling and Diagnosing the Cardiovascular System
    http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.wcnn95.abs.html
   Artificial Neural Networks in Medicine
    http://www.emsl.pnl.gov:2080/docs/cie/techbrief/NN.techbrief.html
   Neural Networks by Eric Davalo and Patrick Naim
   Learning internal representations by error propagation by Rumelhart, Hinton and Williams
    (1986).
   Klimasauskas, CC. (1989). The 1989 Neuron Computing Bibliography. Hammerstrom, D.
    (1986). A Connectionist/Neural Network Bibliography.
   DARPA Neural Network Study (October, 1987-February, 1989). MIT Lincoln Lab. Neural
    Networks, Eric Davalo and Patrick Naim
   Assimov, I (1984, 1950), Robot, Ballatine, New York.
   Electronic Noses for Telemedicine
    http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.ccc95.abs.html
   Pattern Recognition of Pathology Images
    http://kopernik-eth.npac.syr.edu:1200/Task4/pattern.html

								
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