Rationalizing Neural Predictions

A comparative analysis of two neural network predictions for performance and emissions in a biodiesel fuelled diesel Engine

Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models

The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This   work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on De...

Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

Continued development of an empirical function for predicting and rationalizing protein-protein binding affinities.

Here we summarize recent work on the continued development of our fast and simple empirical equation for predicting and structurally rationalizing protein-protein and protein-peptide binding affinities. Our empirical expression consists of six regression-weighted physical descriptors and derives from two key simplifying assumptions: (1) the assumption of rigid-body association and (2) the assum...