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Artificial Neural Networks. 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

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Artificial Neural Networks

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what are artificial neural networks
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.
  • 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.
biological inspiration
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.


artificial neural networks anns
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 understood


artificial neuron


Artificial Neuron
how does it work
How does it work
  • Neural 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 ann


Training an ANN
  • Adjust weights such that the application of inputs produce desired
  • outputs (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
example will the teacher give a quiz

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.
comparison to other methods
Comparison to other methods
  • Simulated Annealing
    • More accurate results
    • Much slower
  • Genetic Algorithms
    • More accurate results
    • Slower
application of anns
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
application cont
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.
  • Computing 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 models
    • Find optimums, recognize patterns
works cited
Works Cited