An Overview of Multilayer Perceptron
Definition of a multilayer perceptron
Multilayer perceptrons are feed forward artificial neural network models that are used to map out groups of input data onto appropriate sets of outputs. The models are made up of multiple layers of nodes in a directed graph and each of the layers are connected with the adjacent one, hence the name multilayer perceptron.
Multilayer perceptrons are generally made up of three or more layers. These may consist of an input layer, output layer and one or more hidden layers. They are considered deep neural networks because each layer contains non-linearly activating nodes. Basically the nodes of one layer connect with a specific weight to the nodes of the next layer.
All the nodes in multilayer perceptron are processing elements or neurons with nonlinear activation functions apart from the input nodes. The multilayer perceptrons are unique in the sense that they are modified from the standard linear perceptrons and as such can differentiate between data that has not been separated in a linear manner.
When it comes to training networks, the multilayer perceptron uses backpropagation method. Backpropagation is simply a generalization of the least mean squares algorithm in linear perceptrons. This is a supervised learning technique where learning occurs by changing connection weights after data is processed. Data processing is done based on the amount of errors in the output compared to the expected results.
Activation functions used by multilayer perceptron
Where multilayer perceptrons have linear activation functions in every neuron then linear functions will map out the weighted inputs to the outputs of every neuron. This makes it easy to prove using linear algebra that the layers in a multilayer perceptron can be decreased to the typical or normal two layer input and output models.
Multilayer perceptrons are considered different because every neutron uses a non linear function which is specifically developed to represent the frequency of action potentials of biological neurons in the brain. The non-linear functions in multilayer perceptrons can therefore be modeled in different ways including:
- The sigmoid function
- Logistic function
- Rectifier and soft plus function
- Radial basis functions
Application of multilayer perceptron
Learning in multilayer perceptrons mostly takes place through the backpropagation algorithm. This is the standard algorithm for supervised learning patterns and recognition processes. Multilayer perceptrons are also the subject in many ongoing researches in both parallel distributed processing and computational neuroscience.
Multilayer perceptrons are well researched because they have the ability to solve problems in a stochastic manner and this facilitates solving of extremely complex problems such as the fitness approximation.
In the 1980s multilayer perceptrons gained much popularity and were used in machine learning solutions. They found applications in different fields such as:
- Speech recognition
- Machine translation software
- Image recognition
However, the 1990s brought forth strong competition for multilayer perceptrons from simpler support vector machines. In the recent times, there is some renewed interest in multilayer perceptrons because of the successes associated with deep learning and backpropagation networks.
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