Rohit Ray ESE 251 What are Artificial Neural Networks ANN are inspired by models of the biological nervous systems such as the brain Novel structure by which to process information Number of highly interconnected processing elements neurons working in unison to solve specific problems ID: 673504
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Artificial Neural Networks
Rohit RayESE 251Slide2
What are Artificial Neural Networks?
ANN are inspired by models of the biological nervous systems such as the brainNovel structure by which to process informationNumber of highly interconnected processing elements (neurons) working in unison to solve specific problems.
Recent Development
First artificial neuron -1943 by Warren McCulloch and Walter Pits.
But the technology available at that time did not allow them to do too much.Slide3
Biological Inspiration
Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.
An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.
The nervous system is build by relatively simple units, the neurons, so copying their behaviour and functionality should be the solution.Slide4
From http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.htmlSlide5
Artificial Neural Networks (ANNs),
Work in the same way as the brain's neural network.An artificial neuron has a number of connections or inputs.It is based on the belief that the way the brain works is all about making the right connections
Are good for prediction and estimation when:
Inputs are well understood
Output is well understoodSlide6
From http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.htmlSlide7
Artificial Neuron
From http://www.uic.edu/classes/idsc/ids572cna/Neural%20Networks_1.pdfSlide8
Example of a ANNSlide9
How does it work
Neural Network TrainingTraining - process of setting the best weights on the edges connecting all the units in the
network
Use
the training set to calculate weights such that
ANN output
is as close as possible to the desired output for
as many
of the examples in the training set as possibleSlide10
Training an ANN
Adjust weights such that the application of inputs produce desiredoutputs (as close as possible) Input data is continuously applied, actual outputs calculated, and weights are adjusted
Weights should converge to some value after many rounds of training
Supervised training
Adjust weights such that differences between desired and actual outputs are minimized
Desired output: dependent variable in training data
Each training example specifies {independent variables, dependent variable}
Unsupervised training
No dependent variable specified in training data
Train the NN such that similar input data should generate same output
From http://www.uic.edu/classes/idsc/ids572cna/Neural%20Networks_1.pdfSlide11
Example: Will the teacher give a quiz?
To help solve this question a programmer is provided with the following options
The teacher loves giving quizzes = 0.2.
The teacher has not given a quiz in two weeks = 0.1.
The teacher gave the last three quizzes on Fridays = 0.3.
The sum of the input weights equals 0.6.
The threshold assigned to that neuron is 0.5. In this case, the net value of the neuron exceeds the threshold number so the artificial neuron is fired. This process occurs again and again in rapid succession until the process is completed.
If the ANN is wrong, and the teacher does not give a quiz on Friday, then the weights are lowered.
Each time a correct connection is made, the weight is increased. The next time the question is asked, the ANN will be more likely to answer correctly.
The proper connections are weighted so that there is more chance that the machine will choose that connection the next time. After hundreds of repeated training processes, the correct neural network connections are strengthened and remembered, just like a memory in the human
brain
A
computer can make millions of trial-and-error attempts at lightning speed.
http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.html#ixzz0V6k2i38hSlide12
Comparison to other methods
Simulated AnnealingMore accurate resultsMuch slower
Genetic Algorithms
More accurate results
SlowerSlide13
Application of ANNs
Broad applicability to real world business problems. Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including: sales forecasting
industrial process control
customer research
data validation
Risk management
target marketing Slide14
Application Cont.
MedicineRecognizing diseases from various scans
no need to provide a specific algorithm on how to identify the
disease
Modeling Parts of the Human body
cardiovascular system must mimic the relationship among physiological variables (i.e., heart rate, systolic and diastolic blood pressures, and breathing rate
)
specific to an individual (physical condition)
Instant Physician(1980’s)
Given a set
of
symptoms
it will then find the full stored pattern that represents the "best" diagnosis and treatment. Slide15
Conclusion
Computing world lots to gain from ANNsAbility to learn by example makes them very flexible and powerfulno need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that
task
Regularly used in medicine and business
Used to make models
Find optimums, recognize patternsSlide16
Works Cited
http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.htmlhttp://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
http://www.uic.edu/classes/idsc/ids572cna/Neural%20Networks_1.pdf