A step-by-step analysis of neural network based-feedforward Neural network

A feedforward neural network is a artificial neural network wherein connections the the between does not form a units. As such, it is different from recurrent neural networks.
The Feedforward neural network was the I and simplest type of artificial neural network devised. [citation needed] In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if Any) and to the output nodes. There are no cycles or loops in the network.

Feedforward Neural Network is an artificial neural network, and there is no cycle, one-way transmission, is the simplest artificial neural network.

A similar neuron is described by Warren McCulloch and Walter Pitts in the 1940s.
Warren McCulloch and Walter Pitts in about 1940 years, presented Feedforward neural networks.
This is the result can is found in Peter Auer, Harald burgsteiner and Wolfgang maass "a learning rule for very simple universal a Pproximators consisting of a single layer of perceptrons ".
Peter Auer, Harald Burgsteiner and Wolfgang Maass describes this as a simple universal approximation.

A Two-layer Neural network capable of calculating XOR. The numbers within the neurons represent each neuron ' s explicit threshold (which can is factored out so, all neurons H Ave The same threshold, usually 1). The numbers that annotate arrows represent the weight of the inputs. This net assumes that if the threshold is not reached and zero (not-1) is output. Note that the bottom layer of the ' inputs ' isn't always considered a real neural network.
Two-layer neural networks that compute XOR. The number within the neuron represents an explicit threshold for each neuron (Perceptron layer) (which can be decomposed so that all neurons have the same threshold, usually 1). The number of the annotation arrows represents the weight of the input. The network assumes zero output if the threshold is not reached. Please note that the last layer (output layer) input is not a true neural network layer.

References

1, Zell, Andreas (1994). Simulation Neuronaler Netze [simulation of Neural Networks] (in German) (1st ed.). Addison-wesley. P. 73. ISBN 3-89319-554-8.
2,jump up ^ Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple universal approximators consisting to A single layer of Perceptrons" (PDF). Neural Networks. 21 (5): 786–795. doi:10.1016/j.neunet.2007.12.036. PMID 18249524.
3,jump up ^ Roman M. balabin; Ravilya Z. Safieva; Ekaterina I. Lomakina (2007). "Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline Propert IES prediction ". Chemometr Intell Lab. 88 (2): 183–188. doi:10.1016/j.chemolab.2007.04.006.
4,jump up ^ Tahmasebi, Pejman; Hezarkhani, Ardeshir (January 2011). "Application of a Modular feedforward neural network for Grade estimation". Natural Resources. 20 (1): 25–32. Doi:10.1007/s11053-011-9135-3.