In the present study, modified evacuated tube solar collector (METSC) with a bypass pipe utilizing copper oxide/distilled water (Cu2O/DW) nanofluid is experimented. Then, performance of METSC was predicted through Artificial Neural Networks (ANNs) techniques. The input variables were different volumes storage tank from 5 to 8 l, various diameters 6 10 mm, and volumetric concentration 0 0.04. Al...

In this work we want to analyse the behaviour of two classic Artificial Neural Network models respect to a data complexity measures. In particular, we consider a Radial Basis Function Network and a MultiLayer Perceptron. We examine the metrics of data complexity known as Measures of Separability of Classes over a wide range of data sets built from real data, and try to extract behaviour pattern...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

In this paper, results are presented from a comprehensive series of studies aimed at assessing the suitability of multilayered perceptron (MLP) and radial basis function (RBF) networks for use in embedded, microcontroller-based, condition monitoring and fault diagnosis (CMFD) applications. Our assessment criteria include the performance of each classifier on a range of CMFD-related problems, su...

Even though multilayer perceptrons and radial basis function networks belong to the class of artificial neural networks and they are used for similar tasks, they have very different structures and training mechanisms. So, some researchers showed better performance with radial basis function networks, while others showed some different results with multilayer perceptrons. This paper compares the...

In recent years, the study of hot deformation behavior various materials is significantly marked by an increasing utilization artificial neural networks, which are frequently employed for a flow curve description. This specific kind description commonly solved via Feed-Forward Multi-Layer Perceptron architecture and rarely Radial Basis architecture. Both network architectures compared to assess...

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consi...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

As the demand for multilingual speaker recognizers increases, the development of systems which combine automatic speaker and gender identification, models becomes increasingly important. In this work a speaker and gender identification system is developed using multilingual speech signal as input. MFCCs and delta-MFCCs, LPC, LPCC , Formants ,ZCR are used to build modal for classification and to...

Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It is shown in this paper that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron sub-networks, is...