Soft Computing Techniques for Mishaps Prediction

Soft Computing Techniques for Mishaps Prediction

An attempt is made to implement the soft computing techniques in the prediction of the mishaps behaviors. The main objective of this paper is to implement the Multilayer Perceptron (MLP) neural network topology in mishap analysis and prediction. Efforts are made to summarize past research on the road mishaps causes and their analysis techniques and point out their weaknesses. Indicate new ideas...

Software reliability prediction by soft computing techniques

In this paper, ensemble models are developed to accurately forecast software reliability. Various statistical (multiple linear regression and multivariate adaptive regression splines) and intelligent techniques (backpropagation trained neural network, dynamic evolving neuro–fuzzy inference system and TreeNet) constitute the ensembles presented. Three linear ensembles and one non-linear ensemble...

Soft-computing techniques and ARMA model for time series prediction

The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with art...

Investigating electrochemical drilling (ECD) using statistical and soft computing techniques

In the present study, five modeling approaches of RA, MLP, MNN, GFF, and CANFIS were applied so as to estimate the radial overcut values in electrochemical drilling process. For these models, four input variables, namely electrolyte concentration, voltage, initial machining gap, and tool feed rate, were selected. The developed models were evaluated in terms of their prediction capability with m...

Hydrologic Prediction Using Pattern Recognition and Soft-computing Techniques