This article presents an improvement method of an identified inertia matrix with the neural network model of a robot. A problem of a neural network model is its inability to assure the positive definiteness of an identified inertia matrix. Therefore the method of matrix regularization based on Cholesky decomposition is proposed. Simulation results show significant improvement of the accuracy of...

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

Most data-based predictive modeling techniques have an inherent weakness in that they may give unstable or inconsistent results when the predictor data is highly correlated. Predictive modeling problems of this design are usually under constrained and are termed ill-posed. This paper presents several examples of ill-posed surveillance and diagnostic problems and regularization methods necessary...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

The number of non-farming rural residents surrounding pork farm operations has increased greatly in many pork-producin nations. These non-farming rural residents consider pork farm odour emissions to be a serious health threat. This has led t significant lobbying to limit the expansion and development of pork operations, and consequently reduced economic growth i the pork industry. This has led...

We describe novel Bayesian models for time–frequency inverse modelling of nonstationary signals. These models are based on the idea of a Gabor regression, in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general ...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...