A local unsupervised processing stage is inserted within a neural network constructed to recognize facial expressions. The stage is applied in order to reduce the dimensionality of the input data while preserving some topological structure. The receptive fields of the neurons in the first hidden layer self-organize according to a local energy function, taking into account the variance of the in...

Artificial neuron An artificial neuron is a mathematical 18 function that simulates in a simplified form the func19 tions of biological neurons. Usually, an artificial neu20 ron has four computational functions, namely receives 21 signals through input connections from other neurons 22 or from the environment, sums the input signals, ap23 plies a nonlinear functions (transfer function or activa...

Abstract The phase transition of the two-dimensional U(1) quantum link model on triangular lattice is investigated by employing a supervised neural network (NN) consisting only one input layer, hidden layer two neurons, and output layer. No information studied used when NN training conducted. Instead, artificially made configurations are considered as set. Interestingly, obtained not estimates ...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target o...