RBF Artificial Neural Network Research Articles

Assessment of the Underground Water Contaminated by the Leachate of Waste Dump of Open Pit Coal Mine Based on RBF Neural Network

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.

Modeling Hot Rolling Based on RBF Neural Networks and Thermo-Mechanical Coupling FEM

In the modeling of rolling process, the result from FEM is influenced by the accuracy of the resistance of deformation model of material which is often from the experiment using isothermal compression at elevated temperatures. But the state under high temperature is unstable in lab. And the flow stress model under the lab condition is not consistent to the hot rolling field condition. This paper will employ the RBF artificial neural networks to train the random sample from the field data and get the relation between the rolling force and the parameters of hot rolling. The array of the rolling force and temperature under special condition can be then obtained and the resistance of deformation model will be built by applying the S.EKLUND formula. Finally, the thermo-mechanical coupling FEM with the resistance of deformation model got from RBF method is used to simulate the hot rolling process. The simulated experiment shows the modeling data is fit to the field data.

Prediction of Cutting Consumption Based on Optimization-Making RBF Artificial Neural Network

Excision rate was fitted and predicted via building on cutting parameters affecting cutting machining process with Optimization-Making RBF (OMRBF) Neural Network. Radial basis function was made to select the best optimized distribution density in order to advance the fitting and forecasting capability of RBF. The result of OMRBF was compared with BP Neural Network’s, what showed that the fitting and forecasting accuracy of OMRBF was much higher than BP Neural Network’s.

Analysis of the reference open system interconnection model clustering of by MLP and RBF network toolkit

Algorithm of the classification with representing the set of functions’ taxons by the cortege, which implements the learning process of multilayer artificial neural networks (ANN) MLP and RBF, are proposed to apply for the reference open systems interconnection model (OSI RM). The quality criteria for the definition of the cortege are formed using the magnitudes that characterize the relative number of common parameters for each pair of OSI RM functions. By the experimental studies of the algo-rithm changing the thresholds values in the MLP and RBF ANN models’ constraints it is revealed a nonexisting in the classical OSI RM distribution of the functions between the hierarchical levels that stipulates the development of improved recommendations with the appearance of a new technologies

Synthesis of a Nonparametric Classifier on the Basis of RBF Artificial Neural Networks

We obtain the nonparametric procedure of classification on the basis of combining classical signal processing methods and neural-network technologies. An approach for structural parametric synthesis of the RBF networks constituting the classifier basis is developed. The simulation results confirm the efficiency of the proposed procedure.