Artificial Neural Networks

Rohit RayESE 251

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 DevelopmentFirst artificial neuron -1943 by Warren

McCulloch and Walter Pits.But the technology available at that time did not

allow them to do too much.

Biological InspirationAnimals 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.

From http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.html

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 understoodOutput is well understood

From http://www.scienceclarified.com/scitech/Artificial-Intelligence/Mind-Versus-Metal.html

Artificial NeuronFrom http://www.uic.edu/classes/idsc/ids572cna/Neural%20Networks_1.pdf

Example of a ANN

How does it workNeural Network Training

Training - 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 possible

Training an ANNAdjust 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 trainingSupervised 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.pdf

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#ixzz0V6k2i38h

Comparison to other methodsSimulated Annealing

More accurate resultsMuch slower

Genetic AlgorithmsMore accurate resultsSlower

Application of ANNsBroad 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

Application Cont.Medicine

Recognizing 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.

ConclusionComputing world lots to gain from ANNs

Ability to learn by example makes them very flexible and powerful

no 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 modelsFind optimums, recognize patterns

Works Citedhttp://www.scienceclarified.com/scitech/Artifi

cial-Intelligence/Mind-Versus-Metal.htmlhttp://www.doc.ic.ac.uk/~

nd/surprise_96/journal/vol4/cs11/report.htmlhttp://www.uic.edu/classes/idsc/

ids572cna/Neural%20Networks_1.pdf