Leírás
Artificial neural networks (ANNs) are machine learning methods aimed to find relationships among input and output variables on historical data, i.e. approximate functions that produce minimum error on the observed dataset. ANNs have shown success in solving prediction, classification and association problems.The most commonly used multi-layer perceptron (MLP) network consists of an input layer which loads values from an input vector in each iteration, a hidden layer which uses the summed weighted inputs from the input layer and applies an activation function, and an output layer where the output is produced and the error computed. The most commonly used algorithm for optimizing the ANN error is the gradient descent, while the second-order methods can be also applied. In case of multiple hidden layers, we talk about deep learning neural networks (DNNs) which are able to process huge datasets. They gained its popularity with Big Data platforms and new generations of fast computer processors such as GPU and TPU. By DNNs, different activation functions can be used in each hidden layer, and various methods of feature extraction can be added. In this lecture, the procedure of creating DNN models for prediction and classification in the area of energy management will be presented using R software tool.