Multilayer Perceptron

See: Perceptron Layer, Multilayer Neural Network, Feedforward Neural Network, Backpropagation, Perceptron, Linear Separability.

References

(Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/multilayer_perceptron Retrieved:2018-1-4.
- A multilayer perceptron (MLP) is a class of feedforward artificial neural network. An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. ^[1] ^[2] Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.^[3] Multilayer perceptrons are sometimes colloquially referred to as "vanilla" neural networks, especially when they have a single hidden layer. ^[4]

↑ Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
↑ Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. “Learning Internal Representations by Error Propagation". David E. Rumelhart, James L. McClelland, and the PDP research group. (editors), Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press, 1986.
↑ Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function Mathematics of Control, Signals, and Systems, 2(4), 303–314.
↑ Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, NY, 2009.

(Hosseiniamid & Samanipour, 2015) ⇒ Seyed H. Hosseiniamid, and Mahdi Samanipour. (2015). “Prediction of Final Concentrate Grade Using Artificial Neural Networks from Gol-E-Gohar Iron Ore Plant.” American Journal of Mining and Metallurgy 3, no. 3
- QUOTE: Figure 4. Multilayer perceptron neural network model 1 (5-8-7-7-4)