1. Artificial Neural Networks (ANN)
• Human information processing takes place through the interaction of many
billions of neurons connected to each other, each sending excitatory or inhibitory
signals to other neurons (excite in positive/suppress in negative)
• Human Brain: Parallel Processing
+ excites
- supresses
+ -
-
+ +
- +

2. ANN
• The neuron receives signals from other
neurons, collects the input signals, and
transforms the collected input signal
• The single neuron then transmits the
transformed signal to other neurons

3. ANN
• The signals that pass through the junction, known as synapses, are
either weakened or strengthened depending upon the strength of the
synaptic connection
• By modifying synaptic strengths, the human brain is able to store
knowledge and thus allow certain inputs to result in specific output or
behavior
• Translates into a mathematical model
• Artificial Neural Networks compare weights
– Synopsis is small = -
– Synopsis is large = +
• ON = +
• OFF = -
• Neurons are trained
– Neurons are on (+) or off (-)
• Example: Could be Facial Recognition

4. ANN
• A basic ANN model consists of
– Computational units
– Links
• A unit emulate the functions of a neuron
• Computational units are connected by links with
variable weights which represent synapses in the
biological model (Human Brain)
• Learning Curve: Change synopsis in face recognition
– Changes & learns new info

5. ANN
• The unit receives a weighted sum of all
its input via connections and computes
its own output value using its own
output function
• The output value is then propagated to
many other units via connection
between units

6. Basic Representation
• Parallel Transfer
– Some connections bi-directional, some one-way
• Variation of algorithms
– 2 levels
– Multi-levels
• y=f (x1, x2, x3)
– where is is a transform function (linear or non-linear)

7. Basic Representation
Sum: Netj = Sum of Wji Xi
Transfer: Yj = F (Netj )
S u m Transfer
X1
X2
X3 jth Computational
Unit
Weights
Wj1
Wj2
Wj3
Yj
Output Path

8. ANN
• Computational units in ANN are
arranged in layers - input, output, and
hidden layers
• Units in a hidden layer are called
hidden units

9. Hidden Units
• Hidden unit is a unit which represents
neither input nor output variables
• It is used to support the required
function from input to output

13. ANN Learning Algorithm
Supervised Learning Unsupervised Learning
Binary Input Continued Binary Continued
Hopfield Net Perceptron ART I ART II
Boltzman- Backpropagation Self-organizing
Machine (popular algorithm widely used) Map

14. Backpropagation
• The algorithm is a learning rule which
suggests a way of modifying weights to
represent a function from input to output
• The network architecture is a
feedforward network where
computational units are structured in a
multi-layered network: an input layer,
one or more hidden layer(s), and an
output layer

15. Backpropagation
• The units on a layer have full
connections to units on the adjacent
layers, but no connection to units on the
same layer

16. Backpropagation
• Calculate the difference (error) between
the expected and actual output value
• Adjust the weights in order to minimize
the error
• Minimize the error by performing a
gradient decent on the error surface

17. Backpropagation
• The amount of the weight change for
each input pattern in an epoch is
proportional to the error
• An epoch is completed after the
network sees all of the input and output
pairs

18. Five Input Var.
Net Working Capital/Total Assets
Retained Earning/Total Assets
EBIT/Total Assets
Market Value of Common
and Preferred Stock/Book Value
of Debt
Sales/Total Assets
Two Output Variables
Solvent Firms
Bankrupt Firms
An ANN model to Predict a Firm’s Bankruptcy

19. Advantages of ANN
• Parallel Processing
• Generalization
– a great deal of noise and randomness can
be tolerated
• Fault tolerance
– damage to a few units and weights may
not be fatal to the overall network
performance

20. Properties of ANN
• No special recovery mechanism is
required for incomplete information
• Learning capability

21. Disadvantages of ANN
• Black box
– Difficulty to interpret information on the
network
• Complicated Algorithms