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Artificial Neural Network
Ali Raza
University of Gujrat
Pakistan
Defination
• ANNs is biological inspired model
• Simulate the way in which human brain is
working
• ANN gather the knowledge by detecting
patterns and relationship in data
• It learnt from experience not from
programming
How it is formed
• ANN is formed from hundreds of Single Units,
Artificial Neurons or Processing elements,
connected with coefficients
• Coefficient: Which constitute the neural
structure and are organized in layers
• Power of Neural Computations comes from
connecting Neurons.
ANN
• Artificial Neurons or Processing Elements
Consist of
i. Weighted Input
ii. Transfer Function
iii. One Output
Cont…
• Behavior of Neural Network is Determined by
transfer function of its Neurons, by
architecture itself, by learning rules
• Weights are the adjustable parameters, in that
sense, Artificial Neural Network is the
parameterized system
Cont…
• Weighted sum of inputs constitutes the
activation of neurons
• The input signal pass through the transfer
function and produce output of neuron
• Transfer function produce Non linearity of the
function
Cont..
• The inter-unit connections are optimized until
the error in predictions is minimized and the
network reaches the specified level of
accuracy.
• Once the network is trained and tested it can
be given new input information to predict the
output
Cont…
• Many types of neural networks have been
designed already and new ones are invented
every week but all can be described by the
transfer functions of their neurons, by the
learning rule, and by the connection formula
• ANN represents a promising modeling
technique, especially for data sets having non-
linear relationships which are frequently
encountered in pharmaceutical processes
Cont…
• In term of model specification ANN not
Required the information about the data
source but the weights must be estimated,
they require large number of training set
• ANN can combine and incorporated both
literature based and experimental based data
to solve problem
Application
• Pattern Recognition
• Prediction and modeling
• Face Recognition
• Iris Recognition
• Optical Path Finding
etc

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Artificial Neural Network

  • 1. Artificial Neural Network Ali Raza University of Gujrat Pakistan
  • 2. Defination • ANNs is biological inspired model • Simulate the way in which human brain is working • ANN gather the knowledge by detecting patterns and relationship in data • It learnt from experience not from programming
  • 3. How it is formed • ANN is formed from hundreds of Single Units, Artificial Neurons or Processing elements, connected with coefficients • Coefficient: Which constitute the neural structure and are organized in layers • Power of Neural Computations comes from connecting Neurons.
  • 4. ANN • Artificial Neurons or Processing Elements Consist of i. Weighted Input ii. Transfer Function iii. One Output
  • 5. Cont… • Behavior of Neural Network is Determined by transfer function of its Neurons, by architecture itself, by learning rules • Weights are the adjustable parameters, in that sense, Artificial Neural Network is the parameterized system
  • 6. Cont… • Weighted sum of inputs constitutes the activation of neurons • The input signal pass through the transfer function and produce output of neuron • Transfer function produce Non linearity of the function
  • 7. Cont.. • The inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. • Once the network is trained and tested it can be given new input information to predict the output
  • 8. Cont… • Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula • ANN represents a promising modeling technique, especially for data sets having non- linear relationships which are frequently encountered in pharmaceutical processes
  • 9. Cont… • In term of model specification ANN not Required the information about the data source but the weights must be estimated, they require large number of training set • ANN can combine and incorporated both literature based and experimental based data to solve problem
  • 10. Application • Pattern Recognition • Prediction and modeling • Face Recognition • Iris Recognition • Optical Path Finding etc