Single-Layer Feedforward Neural Network

From GM-RKB
Jump to navigation Jump to search

A Single-Layer Feedforward Neural Network is a feedforward neural network that is a single-layer neural network.



References

2017

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Feedforward_neural_network#Single-layer_perceptron Retrieved:2017-12-17.
    • The simplest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. In this way it can be considered the simplest kind of feed-forward network. The sum of the products of the weights and the inputs is calculated in each node, and if the value is above some threshold (typically 0) the neuron fires and takes the activated value (typically 1); otherwise it takes the deactivated value (typically -1). Neurons with this kind of activation function are also called artificial neurons or linear threshold units. In the literature the term perceptron often refers to networks consisting of just one of these units. A similar neuron was described by Warren McCulloch and Walter Pitts in the 1940s.

      A perceptron can be created using any values for the activated and deactivated states as long as the threshold value lies between the two.

      Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. It calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights, thus implementing a form of gradient descent.

      Single-unit perceptrons are only capable of learning linearly separable patterns; in 1969 in a famous monograph entitled Perceptrons, Marvin Minsky and Seymour Papert showed that it was impossible for a single-layer perceptron network to learn an XOR function (nonetheless, it was known that multi-layer perceptrons are capable of producing any possible boolean function).

      Although a single threshold unit is quite limited in its computational power, it has been shown that networks of parallel threshold units can approximate any continuous function from a compact interval of the real numbers into the interval [-1,1]. This result can be found in Peter Auer, Harald Burgsteiner and Wolfgang Maass "A learning rule for very simple universal approximators consisting of a single layer of perceptrons".[1]

      A multi-layer neural network can compute a continuous output instead of a step function. A common choice is the so-called logistic function: : [math]\displaystyle{ f(x) = \frac{1}{1+e^{-x}} }[/math] With this choice, the single-layer network is identical to the logistic regression model, widely used in statistical modeling. The logistic function is also known as the sigmoid function. It has a continuous derivative, which allows it to be used in backpropagation. This function is also preferred because its derivative is easily calculated: : [math]\displaystyle{ f'(x) = f(x)(1-f(x)) }[/math] .

      (The fact that f satisfies the differential equation above can easily be shown by applying the Chain Rule.)

  1. Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple universal approximators consisting of a single layer of perceptrons" (PDF). Neural Networks. 21 (5): 786–795. PMID:18249524.

2008