‎Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated‎. ‎In this paper‎, ‎we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties‎, ‎and we prove the global ...

estimation (forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. thus, accuracy of the estimation is highly desirable. hibrid regression neural network is an approach proposed in this paper to obtain better fitness in comparison with regression analysis and the neural network methods. comparing the estimated resul...

This paper addresses a contrastive study between Neural Networks and Rough Sets on data classification. The experiments were carried out using the Iris database, of public domain, to evaluate the classification. The confusion matrix method was used to evaluate the performance of these classifiers. With these contrastive experiments, we investigated the capacity of each classifier for applicatio...

This paper presents a novel approach to financial time series analysis and prediction. It is mainly devoted to the problem of forecasting university facility and administrative cost recovery. However, it can also be used in other areas of a similar nature. The methodology incorporates a two-stage hybrid mechanism for selection of prediction-relevant features and for forecasting based on this se...

Artificial intelligence technology has rapidly developed; it has already fused into all walks of life. The paper applies artificial intelligence knowledge to make diagnosis and evaluation on China men table tennis players’ competition technical and tactics, the paper firstly briefly introduces artificial intelligence method and decision-making tree, rough set, artificial neural network, and the...

After the major reasons of water bloom were analyzed, using the rough set theory and principal component analysis respectively to identify the main factors affecting the forecast algal blooms. On this basis, to take advantage of the Libsvm water bloom prediction model and Elman water bloom prediction model for the shortterm prediction of algal blooms phenomenon respectively. Obtained through th...

In internet based Virtual Hospital for power equipment, it is not easy to use the rules of power equipment fault diagnosis from the resources by traditional exoert system. Data mining technique opens a new window for the utilization of the abundant and chaotic data in VH. In this paper, the clustering rule and Radial Basis Function Neural Network based data mining fault diagnosis method for pow...

in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the arti...

A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perceptron, and back-propagation algorithm is described. The described model is capable to deal with rough uncertainty as well as fuzzy uncertainty associated with classification of remotely sensed multi-spectral images. The input vector consists of membership values to linguistic properties while the outp...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...