ARTIFICIAL NEURAL NETWORKS
Outline
Introduction Computation in the brain Artificial Neuron Models Types of Neural Networks
Discussion
What tasks are machines good at doing that humans are not?
What tasks are humans good at doing that machines are not?
What does it mean to learn? How is learning related to intelligence?
What does it mean to be intelligent? Do you believe a machine will ever be built that exhibits intelligence?
If a computer were intelligent, how would you know? What does it mean to be conscious?
Can one be intelligent and not conscious or vice versa?
Types of Applications
Machine learning
Cognitive science
Neurobiology
Mathematics
Philosophy
Usage
Signal processing
Control
Robotics
Pattern recognition
Speech production
Speech recognition
Vision
Financial applications
Data compression
Game playing
Computation in the Brain
The Brain is an Information Processing
System
10 billion nerve cells (neurons)
about 10 000 synapses
Massive parallel information processing
Capabilities of the Brain
Its performance tends to degrade gracefully under partial damage In contrast with most programs / engineered systems
It can learn (reorganize itself) from experience. Partial recovery from damage is possible if healthy units
can learn to take over the functions previously carried out by the damaged areas
It performs massively parallel computations extremely efficiently Complex visual perception
Comparison with Computer
Artificial Neural Networks attempt to bring computers a little closer to the brain's capabilities by imitating certain aspects of information processing in the brain, in a highly simplified way.
A branch of Artificial Intelligence
A loosely modeled system of artificial neurons
based on the human brain / neurons
A network of many very simple processors
(units)
each possibly having a (small amount of) local
memory
Artificial Neural Networks
A neural network can be a processing device, an algorithm, or actual hardware
Training rule weights of connections are adjusted based
presented patterns
Artificial Neural Networks (Ctd.)
Unidirectional communication channels
(connections)
Carry numeric (as opposed to symbolic) data
Units operate
On local data
On inputs received via connections
Properties
Learns from examples / experience to improve their performance
to adapt changes in the environment
to deal with incomplete information or noisy data
Capability of generalization Great potential for parallelism
Computations of components: independent of each other
Benefits
Structure
•A Neural network can be considered as a black box that is able to predict an output pattern when it recognizes a given input pattern.
•Once trained, the neural network is able to recognize similarities when presented with a new input pattern, resulting in a predicted output pattern.
Network Structure
Layered circuit of neurons Neighboring layers completely connected; no other
connections (feedforward network) Arbitrary number of hidden layers allowed, but usually 0 or 1
A Simple Artificial Neuron
•The basic computational element (model neuron) is often called a node or unit. It receives input from some other units, or perhaps from an external source. Each input has an associated weight w, which can be modified so as to model synaptic learning. The unit computes some function f of the weighted sum of its inputs
•Its output, in turn, can serve as input to other units.
•The weighted sum is called the net input to unit i, often written neti.
•Note that wij refers to the weight from unit j to unit i (not the other way around).
•The function f is the unit's activation function. In the simplest case, f is the identity function, and the unit's output is just its net input. This is called a linear unit.
Processing Information
x1 w1j
x2
xi
Yj
wij
w2jNeuron j wij xi
Weights
Output
Inputs
Summations Transfer function
Operation of a Single Neuron
Activation Functions
Step function
Changing the bias weight Wo, moves the threshold location.
Components and Structure Processing Elements Network Structure of the Network
Processing Information by the Network Inputs Outputs Weights Summation Function
Neural Network Fundamentals
The learning process of a Neural Network can be viewed as reshaping a sheet of metal, which represents the output (range) of the function being mapped.
The training set (domain) acts as energy required to bend the sheet of metal such that it passes through predefined points. However, the metal, by its nature, will resist such reshaping.
So the network will attempt to find a low energy configuration (i.e. a flat/non-wrinkled shape) that satisfies the constraints (training data).
Learning Process
Learning can be done in supervised or unsupervised training.
In supervised training, both the inputs and the outputs are provided.
The network processes the inputs and compares its resulting outputs against the desired outputs. Errors are then calculated, causing the system to
adjust the weights which control the network. This process occurs over and over as the weights are
continually tweaked.
Learning Process
Learning: Three Tasks
1. Compute Outputs
2. Compare Outputs with Desired Targets
3. Adjust Weights and Repeat the Process
Neural NetworkApplication Development
Preliminary steps of system development are done ANN Application Development Process
1. Collect Data
2. Separate into Training and Test Sets
3. Define a Network Structure
4. Select a Learning Algorithm
5. Set Parameters, Values, Initialize Weights
6. Transform Data to Network Inputs
7. Start Training, and Determine and Revise Weights
8. Stop and Test
9. Implementation: Use the Network with New Cases
Data Collection and Preparation
Collect data and separate into a training set and a test set
Use training cases to adjust the weights
Use test cases for network validation
Neural Network Architecture
Representative Architectures
Associative Memory Systems Associative memory - ability to recall complete situations
from partial information Systems correlate input data with stored information Hidden Layer Three, Sometimes Four or Five Layers
Recurrent Structure
Recurrent network (double layer) - each activity goes through the network more than once before the output response is produced
Uses a feedforward and feedbackward approach to adjust parameters to establish arbitrary numbers of categories
Example: Hopfield
Neural Network Preparation (Non-numerical Input Data (text, pictures): preparation may involve
simplification or decomposition) Choose the learning algorithm Determine several parameters
Learning rate (high or low) Threshold value for the form of the output Initial weight values Other parameters
Choose the network's structure (nodes and layers) Select initial conditions Transform training and test data to the required format
Training the Network
Present the training data set to the network
Adjust weights to produce the desired output for each of the inputs Several iterations of the complete training set to get a
consistent set of weights that works for all the training data
Learning Algorithms
Two Major Categories Based On Input Format Binary-valued (0s and 1s) Continuous-valued
Two Basic Learning Categories Supervised Learning
Inputs Outputs
Unsupervised Learning Inputs
No desired outputs System decides how to group the input data
Backpropagation
"Backwards propagation of errors". Supervised learning method
Implements Delta rule (gradient descent algorithm) It requires a teacher that knows, or can
calculate, the desired output for any given input. It is most useful for feed-forward networks
(networks that have no feedback, or simply, that have no connections that loop).
Activation function should be is differentiable.
Backpropagation Steps Present a training sample to the neural network. Compare the network's output to the desired output from that
sample. Calculate the error in each output neuron. For each neuron, calculate what the output should have been, and a
scaling factor, how much lower or higher the output must be adjusted to match the desired output. This is the local error.
Adjust the weights of each neuron to lower the local error. Assign "blame" for the local error to neurons at the previous level,
giving greater responsibility to neurons connected by stronger weights.
Repeat from step 3 on the neurons at the previous level, using each one's "blame" as its error.
Training Patterns
10 steps 1000 steps
Training Pattern
The shown pattern has not been learned yet.... the global error is still high
1021 steps
Global error is getting down as training continues.....Deformed and noisy patterns are also used in training.
4000 steps
Training Pattern
8000 steps 12000 steps
Recognition
Too much noise
Backpropagation
Backpropagation (back-error propagation) Most widely used learning Relatively easy to implement Requires training data for conditioning the
network before using it for processing other data
Network includes one or more hidden layers Network is considered a feedforward approach
Externally provided correct patterns are compared with the neural network output during training (supervised training)
Feedback adjusts the weights until all training patterns are correctly categorized
Error is backpropogated through network layers Some error is attributed to each layer Weights are adjusted A large network can take a very long time to train May not converge
Backpropagation
Testing Test the network after training Examine network performance: measure the network’s
classification ability Black box testing Do the inputs produce the appropriate outputs? Not necessarily 100% accurate But may be better than human decision makers Test plan should include
Routine cases Potentially problematic situations
May have to retrain
Unsupervised Learning
Only input stimuli shown to the network Network is self-organizing Number of categories into which the network
classifies the inputs can be controlled by varying certain parameters
Examples Adaptive Resonance Theory (ART) Kohonen Self-organizing Feature Maps
The Self-Organizing Map: An Alternative NN Architecture
Kohonen Self-Organizing Map (SOM)
Unsupervised learning
Weights self-adjust to input pattern
Topology
Unsupervised Neural Networks –Kohonen Learning
Also defined – Self Organizing Map Learn a categorization of input space Neurons are connected into a 1-D or 2-D lattice. Each neuron represents a point in N-dimensional
pattern space, defined by N weights During training, the neurons move around to try and
fit to the data Changing the position of one neuron in data space
influences the positions of its neighbors via the lattice connections
Self Organizing Map – Network Structure
All inputs are connected by weights to each neuron
size of neighbourhood changes as net learns
Aim is to map similar inputs (sets of values) to similar neuron positions.
Data is clustered because it is mapped to the same node or group of nodes
SOM-Algorithm
1. Initialization :Weights are set to unique random values
2. Sampling : Draw an input sample x and present in to network
3. Similarity Matching : The winning neuron i is the neuron with the weight vector that best matches the input vector
i = argmin(j){ x – wj }
SOM - Algorithm4. Updating : Adjust the weights of the winning neuron
so that they better match the input. Also adjust the weights of the neighbouring neurons.
∆wj = η . hij ( x – wj)
neighbourhood function : hij
over time neigbourhood function gets smaller
Result: The neurons provide a good approximation of the input space and correspond
Applications Clustering: explores the similarity between patterns and places
similar patterns in a cluster. data compression data mining.
Classification/Pattern recognition: assigns an input pattern (like handwritten symbol) to one of many classes. associative memory.
Function approximation: finds an estimate of the unknown function f() subject to noise. Various engineering and scientific disciplines
Prediction/Dynamical Systems: forecasts some future values of a time-sequenced data. Prediction differs from Function approximation by considering time factor. Here the system is dynamic and may produce different results for the same input data based on system state (time).
Neural Network types can be classified based on following attributes:• Applications
-Classification-Clustering-Function approximation-Prediction
• Connection Type- Static (feedforward)- Dynamic (feedback)
• Topology - Single layer- Multilayer- Recurrent- Self-organized
• Learning Methods- Supervised- Unsupervised
Types of Neural Networks
Neural Computing Paradigms
Decisions the builder must make Size of training and test data Learning algorithms Topology: number of processing elements and their
configurations Transformation (transfer) function Learning rate for each layer Diagnostic and validation tools
Results in the Network's Paradigm
Neural Network Software
Program in: Programming language Neural network package or NN programming tool Both
Tools (shells) incorporate: Training algorithms Transfer and summation functions
May still need to: Program the layout of the database Partition the data (test data, training data) Transfer the data to files suitable for input to an ANN tool
NN Development Tools
MATLAB NNTOOL Braincel (Excel Add-in) NeuralWorks Brainmaker PathFinder Trajan Neural Network Simulator NeuroShell Easy SPSS Neural Connector NeuroWare
Limitations ofNeural Networks
Do not do well at tasks that are not done well by people
Lack explanation capabilities Limitations and expense of hardware technology
restrict most applications to software simulations Training time can be excessive and tedious Usually requires large amounts of training and test
data
Neural Networks For Decision Support
Inductive means for gathering, storing, and using experiential knowledge
Forecasting ANN in decision support: Easy sensitivity analysis
and partial analysis of input factors The relationship between a combined expert system,
ANN and a DSS ANN can expand the boundaries of DSS
Neural Networks for Regression
Discrete Inputs
Classification
Pattern Recognition
Clustering
Case study: Assessment of Implication of Competitiveness on Human
Development of Countries via Data Envelopment Analysis and Cluster Analysis
OUTPUT- ORIENTED SUPER EFFICIENCY DEA
Calculation of countries’ efficiency scores considering WEF scores as
input and HDI Scores as output
CLUSTER ANALYSIS by SOM
Classification of the countries based on WEF and HDI scores
WEF Scores- Basic requirements - Efficiency enhancers- Innovation and sophistication factors
HDI Scores- Life expectancy at birth
- Combined gross enrollment ratio for primary, secondary and tertiary schools
- GDP / capita
Analyzing the evolution of countries in competitiveness and human development perspectives
Iterative clustering
[2 X 4] [2 X 3]
[5 X 1] [2 X 2]
Results
Changes over years
Modeling construction problems using ANN
Homework
Find three articles from your work domain utilizing ANNs to solve a problem, to help decision making, etc. Scholar.google.com Sciencedirect.com www.library.itu.edu.tr
Select one of them to criticize in one or two paragraphs. How are they using ANNs? What are the inputs and outputs? Do you think it is appropriate to use ANNs for that domain? What
other technique can be used? Submit the full paper of the selected article along with your
criticism. Submit abstracts for the other two.