Download Neuron and Perceptron

Basic Computer Application
Unit 2:Artificial neural network
Bo Li (李波)
[email protected]
Xi’an Jiaotong University
Basic Computer Application
Content
1.
An Experiment
2.
Biological neurons
3.
Artificial neuron
4.
Perceptron
5.
Artificial neural network
6.
Computational Intelligence
7.
Machine Learning
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1. An Experiment
Pigeons
experts
as art
(Watanabe et
al. 1995)
Experiment:
Pigeon
in Skinner box
Present
paintings of
two different artists
(e.g. Chagall / Van
Gogh)
Reward
for pecking
when presented a
particular artist (e.g.
Van Gogh)
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Experiment result

Pigeons were able to discriminate between
Van Gogh and Chagall with 95% accuracy


when presented with pictures they had been
trained on
Discrimination still 85% successful for
previously unseen paintings of the artists
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Why?

Pigeons do not simply memorise the
pictures

They can extract and recognise patterns
(the ‘style’)

They generalise from the already seen to
make predictions

This is what neural networks (biological
and artificial) are good at (unlike
conventional computer)
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2.Biological neurons
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Schematic
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3.An artificial neuron

An artificial neuron is a mathematical
function conceived as a model of
biological neurons.

Artificial neurons are the
constitutive units in an artificial
neural network.

Depending on the specific model used
they may be called a semi-linear unit,
Nv neuron, binary neuron, linear
threshold function, or McCulloch–Pitts
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
Neuron
vs.

Node
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Structure of a node:
Squashing function limits node output:
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Synapse vs. weight
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Component


3 Core parts

receives one or more inputs (representing
dendrites)

sums them

produce an output (representing a neuron's
axon).
Usually the sums of each node are
weighted, and the sum is passed
through a non-linear function known as
an activation function or transfer
function.
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2 functions

Transfer function



Type：

sigmoid shape

piecewise linear functions

step functions.
monotonically increasing, continuous,
differentiable and bounded.
Thresholding function

is inspired to build logic gates referred to as
threshold logic
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4.perceptron
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Learning/Training
Train
Set
Initial:
w1
= w2 = w3=0.5
Threshold=0.8
Rules
1
The
weights are
increased by 10% if
the output produced is
less than the output
data
The
weights are
decreased by 10% if
the output produced is
greater than the
output data.
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Training and Calculating

Training
1
0.8

Calculating
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Difficulty
Two classes of points, and two of the infinitely many linear
boundaries that separate them.
Even though the boundaries are at nearly right angles to one
another, the perceptron algorithm has no way of choosing between
them.
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5. Artificial Neural Networks?

Models of the brain and nervous system

Highly parallel

Process information much more like the brain
than a serial computer

Learning

Very simple principles

Very complex behaviours

Applications

As powerful problem solvers

As biological models
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ANNs – The basics
ANNs
incorporate
the two
fundamental
components of
biological neural
nets:
1.
Neurones (nodes)
2.
Synapses (weights)
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Multi-layer networks &Feed-forward
nets

Several layers of perceptions can be combined to create multilayer neural
networks.

The output from each layer becomes the input to the next layer.

The first layer is called the input layer, the middle layers are called the
hidden layers and the last layer is called the output layer.

Neural networks can be used when enough pre-established inputs and
outputs exist to train the network.
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Feeding data through the net
(1  0.25) + (0.5  (-1.5)) = 0.25 + (-0.75)
=
- 0.5
Squashing:
1
 0.3775
0.5
1 e
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Data

Data is presented to the network
activations in the input layer

Examples


Pixel intensity (for pictures)

Molecule concentrations (for artificial nose)

Share prices (for stock market prediction)
the
form
Data usually requires preprocessing


in
Analogous to senses in biology
How to represent more abstract data, e.g. a name?

Choose a pattern, e.g.

0-0-1 for “Chris”

0-1-0 for “Becky”
of
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Learning algorithms

Weight
settings
determine
behaviour of a network
the
 How can we find the right weights?
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Computational intelligence


Artificial Neural Networks

Connectionist Systems

Computational approach

Modeling the way the brain solves problems
Computational intelligence (CI)

the ability of a computer to learn a specific task
from data or experimental observation.

a set of nature-inspired computational
methodologies and approaches to address complex
real-world problems to which mathematical or
traditional modelling can be useless
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CI -five techniques


The methods used are close to the human's
way of reasoning

uses inexact and incomplete knowledge

able to produce control actions in an adaptive
way.
CI therefore uses a combination of five
main complementary techniques.

The fuzzy logic

artificial neural networks

evolutionary computing

learning theory

probabilistic methods
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Human Beings Intelligence

Computational Intelligence is thus a
way of performing like human beings.

Indeed, the characteristic of
"intelligence" is usually attributed
to humans.

More recently, many products and items
also claim to be "intelligent", an
attribute which is directly linked to
the reasoning and decision making.
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Machine learning

scientific discipline

explores the construction and study of algorithms
that can learn from data.

artificial intelligence and optimization

ML algorithms operate by


building a model based on inputs and using that to
make predictions or decisions

rather than following only explicitly programmed
instructions.
Example applications

spam filtering, optical character recognition (OCR),
search engines and computer vision.
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Summary
1.
An Experiment
2.
Biological neurons
3.
Artificial neuron
4.
Perceptron
5.
Artificial neural network
6.
Computational Intelligence
7.
Machine Learning
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Thank you
Bo Li (李波)
[email protected]
Xi’an Jiaotong University