Adaline Neural Network

Posted By on May 16, 2016


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The back-propagation Algorithm - a mathematical approach
Radial Basis Function Network

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network.

The network uses memistors. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.

The difference between Adaline and the standard (McCulloch–Pitts) perceptron is that in the learning phase the weights are adjusted according to the weighted sum of the inputs (the net). In the standard perceptron, the net is passed to the activation (transfer) function and the function’s output is used for adjusting the weights.

Adaline is a multiple layer neural network with multiple nodes where each node accepts multiple inputs and generates one output. Given the following variables:as

  • x is the input vector
  • w is the weight vector
  • n is the number of inputs
  • \theta some constant
  • y is the output of the model

then we find that the output is y=\sum_{j=1}^{n} x_j w_j + \theta. If we further assume that

  •  x_{n+1} = 1
  • w_{n+1} = \theta

then the output further reduces to the dot product of x and w: y=x \cdot w

 

The back-propagation Algorithm - a mathematical approach
Radial Basis Function Network

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Posted by Akash Kurup

Founder and C.E.O, World4Engineers Educationist and Entrepreneur by passion. Orator and blogger by hobby

Website: http://world4engineers.com