Due to remarkable capabilities of artificial neural networks (ANNs) such as generalization and nonlinear system modeling, ANNs have been extensively studied and applied in a wide variety of applications (Amiri et al., 2007; Davande et al., 2008). The rapid development of ANN technology in recent years has led to an entirely new approach for the solution of many data processing-based problems, u...

Minimum description length (MDL) principle is one of the wellknown solutions for overlearning problem, specifically for artificial neural networks (ANNs). Its extension is called representational MDL (RMDL) principle and takes into account that models in machine learning are always constructed within some representation. In this paper, the optimization of ANNs formalisms as information represen...

Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (ANNs). PCA was used to perform feature extraction in the first stage of modelling, while artif...

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for...

While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the m...

Artificial Neural Networks (ANNs) are based on an abstract and simplified view of the neuron. Artificial neurons are connected and arranged in layers to form large networks, where learning and connections determine the network function. Connections can be formed through learning and do not need to be ’programmed.’ Recent ANN models lack many physiological properties of the neuron, because they ...

Accurate estimation of parameters during transient and steady state is required for controlling of Induction motor. Artificial neural networks (ANNs) based online identification of induction motor parameters are presented. ANNs such as feed forward network is used to develop an ANN as a memory for remembering the estimated parameters and for computing the parameters during transients. Simulatio...

Since artificial neural networks (ANNs) can approximate any function, they have been applied in many fields including hydrology. In hydrology, there are important issues such as flood estimation and predicting rainfall-runoff in a certain area. In this presentation, we briefly introduce a popular feed-forward neural network model, so called “multi-layer perceptron (MLP)”, and review its applica...