Artificial Neural Network

Neural networks have been used with computers since the 1950s. Through the years, many different models have been presented. The perceptron is one of the earliest neural networks. It was an attempt to understand human memory, learning and cognitive processes. To construct a computer capable of «human-like thought», the researchers have used the only working model they have available – the human brain. However, the human brain as a whole is far too complex to model. Rather, the individual cells that make up the human brain are studied. Following is introduced the schema of the most used artificial neural network.

Multilayer Perceptron

For the task of predicting the indexes, we’ll be using the so called multilayer feed forward network which is the best choice for this type of application. In a feed forward neural network, neurons are only connected forward. Each layer of the neural network contains connections to the next layer, but there are no connections back. Typically, the network consists of a set of sensory units (source nodes) that constitute the input layer, one or more hidden layers of computation nodes, and an output layer of computation nodes. In its common use, most neural networks will have one hidden layer, and it’s very rare for a neural network to have more than two hidden layers. The input signal propagates through the network in a forward direction, on a layer by layer basis. These neural networks are commonly referred as multilayer perceptrons (MLPs). Shown below is a simple MLP with 4 inputs, 1 output, and 1 hidden layer.