The aim of this paper is to compare what can be computed with what can be learnable by Artificial Neural Networks (ANN). We will approach the learnability problem in ANN by fixing a particular class of ANN: the weightless neural networks (WNN) ([4)) and restricting ourselves to a particular learning paradigm: learning to recognise sentences of a formallanguage. We will approach the computabilit...

The objective of this paper is to develop and test a model of cost estimating for the shell and tube heat exchangers in the early design phase via the application of artificial neural networks (ANN). An ANN model can help the designers to make decisions at the early phases of the design process. With an ANN model, it is possible to obtain a fairly accurate prediction, even when enough and adequ...

1 Terje Kristensen, The Department of Computer Science, Bergen University College, Nygårdsgaten 112, N-5020 Bergen , Norway. E-mail: [email protected]. Abstract The human brain and related concepts such as learning, knowledge and computing are discussed. The Artificial Neural Network (ANN) paradigm is introduced as a model of computing. Some aspects of the brain and a neural network such as knowledge ...

In the process of learning we may presume the neural networks are simplified models of the biological neurons system. The Artificial Neural Network ( ANN ) is an information processing system which is inspired by brain learning system. It is assumed that brain is composed of a large number of highly interconnected processing elements working in groups to solve specific problems. Various network...

With an optimal network topology and tuning of hyperparameters, artificial neural networks (ANNs) may be trained to learn a mapping from low level audio features to one or more higher-level representations. Such artificial neural networks are commonly used in classification and regression settings to perform arbitrary tasks. In this work we suggest repurposing autoencoding neural networks as mu...

We may presume the neural networks are simplified models of the biological neurons system. The Artificial Neural Network (ANN) is an information processing system which is inspired by brain learning system. It is assumed that brain is composed of a large number of highly interconnected processing elements working in groups to solve specific problems. Various networks and algorithms have been pr...

Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly...

The neural network is traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term may refer to either biological neural networks, made up of real biological neurons, or artificial neural networks, for solving artificial intelligence problems. T...

In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...