This package provides a feedforward Artificial Neural Network (ANN) optimized by Genetic Algorithm (GA).

Package: | ANN |

Type: | Package |

Version: | 0.1.4 |

Date: | 2012-04-06 |

License: | GPL-3 |

LazyLoad: | yes |

An artificial neural network is based on the mathematical representation of a neural network or the human brain works. The artificial neural network (ANN) has neurons and synapses, similar to the neural network. The synapses are the connections between the neurons which are represented as weight (*w_i*). There are several connection types that exist to solve different kinds of problems. In this package, the feedforward artificial neural network is used. In this type of network, the information is gathered into the input layer which forwards the information through the network to the output layer.

The ANN consists of different layers. The input layer takes the input data, then distributes it to the connections that connect the hidden layer(s) and the input layer. The neuron(s) in the hidden layer(s) process(es) the summation of the information received from the connections of the input layer. Then it processes the summations with its activation function and distributes the result to the next layer. This process continues down through the layers to the output layer. The neuron(s) of the output layer process(es) the summation of the information received from the connections of the hidden layer. Then each neuron processes the summation with its activation function. The output of the activation function is the output of the ANN.

In this package, the activation function is sigmoid: *f(x) = \frac{1}{1+e^{-x}}*. An example of a formula for a 3-layer feedforward ANN (the input layer, one hidden layer and the output layer) with one output would be:

*f(∑_{j=1}^{N_{hidden \; layer}} w_{2,j,1}f(∑_{i=1}^{N_{input \; layer}} w_{1,i,j} input_i + bias_{1,j})+bias_{2,1}) =output*

*w_{k,i,j}*, means weight from the *i*th neuron on the *k*th layer to the *j*th neuron on the next layer. The *bias_{k,j}* is similar to a constant in a least square regression. Each neuron of the hidden layer and of the output layer has a bias and the neurons add the bias to the summation. The weights of the connections must be estimated. The learning phase of the ANN can be supervised or unsupervised. In this package supervised learning has been used. Supervised learning consists of giving inputs to the ANN and adjusting the weight to minimize the sum of the differences between the predicted output given by the ANN and the desired output. The mean squared error * MSE =\frac{1}{N} ∑(y^{output}_i - y^{estimated \:\; output}_i)^2* is the criterion to be minimized in this package.

Several methods of minimisation are available. In this package, the Genetic Algorithm is used to solve the problem of estimating the connections' weights. Genetic Algorithm is based on natural evolution. A population of chromosomes is initialised with random numbers. In our case a chromosome is an array of doubles corresponding to weights. This population of chromosomes evolves within every generation; mutation and crossover occur in all chromosomes. Mutation ( *w^{new}_{j,i}=w_{k,i}+mutationRate*(w_{n,j}-w_{m,i}) *) consists of adding a weight *i* from a random chromosome to the difference of the weight *i* of two other random chromosomes multiplied by the mutation rate, where *w_{k,i}* is the *i*th weight of the *k*th chromosome and *k*, *n*, *m* are randomly choosen within the set of chromosomes population and *k*, *n*, *m* are not equal. The crossover consists of changing each chromosome's weights to the mutation weight with the probability of the crossover rate. Then, it replaces the old chromosome with the new one if the MSE of the new chromosome is smaller. This algorithm was originaly developed by Emre Caglar in "CUDAANN r6 project".

Comments, adjustments and/or contributions are welcome.

Francis Roy-Desrosiers

Maintainer: Francis Roy-Desrosiers <francis.roy.desrosiers at gmail.com>

Cross Ss, Harrison Rf, Kennedy RL.. Introduction to neural networks. Lancet 1995; 346: 1075-1079.

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