Perceptron is a machine learning algorithm that helps to provide classified outcomes for computing. It is a kind of a single-layer artificial network with only one neuron and a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. 

Multilayer Perceptron is a class of feed forward artificial neural networks. And the layered feed forward networks are trained by using the static back-propagation training algorithm. For designing and training an MLP perceptron several issues are involved:

  • Multilayer Perceptron Neural Network for flow prediction
  • Comparison to Probability
  • Compensatory Fuzzy Logic
  • Back propagation algorithm for the on-line training of Multilayer Perceptrons
  • Number of hidden layers is selected to use in the neural network
  • A solution that avoids local minima is globally searched
  • Neural networks are validated to test for overfitting
  • Converging to an optimal solution in a reasonable period of time.

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