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Princess Nora University

Artificial Intelligence

Artificial Neural Network (ANN)

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Neural Network

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Perceptron

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Artificial Neural Networks

• When using ANN, we have to define:

– Artificial Neuron Model

– ANN Architecture

– Learning mode

Developing Intelligent Program Systems

Machine Learning : Neural Nets

Neural nets can be used to answer the following:

– Pattern recognition: Does that image contain a face?

– Classification problems: Is this cell defective?

– Prediction: Given these symptoms, the patient has disease X

– Forecasting: predicting behavior of stock market

– Handwriting: is character recognized?

Artificial Neural NetworkLearning paradigms

• Supervised learning: – Teacher presents ANN input-output pairs, – ANN weights adjusted according to error

• Classification• Control• Function approximation• Associative memory

• Unsupervised learning:

– no teacher

• Clustering

ANN capabilities

• Learning• Approximate reasoning• Generalisation capability• Noise filtering• Parallel processing• Distributed knowledge base• Fault tolerance

Main Problems with ANN

• Contrary to Expert sytems, with ANN the Knowledge base is not transparent (black box)

• Learning sometimes difficult/slow

• Limited storage capability

When to use ANNs?• Input is high-dimensional discrete or real-valued (e.g. raw sensor input).

• Inputs can be highly correlated or independent.

• Output is discrete or real valued

• Output is a vector of values

• Possibly noisy data. Data may contain errors

• Form of target function is unknown

• Long training time are acceptable

• Fast evaluation of target function is required

• Human readability of learned target function is unimportant

⇒ ANN is much like a black-box