Neural networks for predicting compressive strength of structural light weight concrete

Advances in neural information processing systems 3

Definitions and basic properties of the Voronoi diagram generalizations of the Voronoi diagram algorithms for computing Voronoi diagrams poisson Voronoi diagrams spatial interpolation models of spatial processes point pattern analysis locational optimization through Voronoi diagrams.

Spatial tessellations. Concepts and Applications of Voronoi diagrams

Cascade Correlation (Cascor) has proved to be a powerful method for training neural networks. Cascor, however, has been shown not to generalise well on regression and some classification problems. A new Cascade network algorithm employing Progressive RPROP (Casper), is proposed. Casper, like Cascor, is a constructive learning algorithm which builds cascade networks. Instead of using weight freezing and a correlation measure to install new neurons, however, Casper uses a variation of RPROP to train the whole network. Casper is shown to produce more compact networks, which generalise better than Cascor.

A Cascade Network Algorithm Employing Progressive RPROP

Previous work in using artificial neural networks for computational stylistics has concentrated on using large, arbitrary network structures. This paper examines the use of the Cascade-Correlation algorithm for the construction of minimal networks. We find that a number of problems in computational stylistics with a large number of variables but a limited number of training examples may be solved successfully without resorting to large networks. The issue of redundancy in the data is also considered.

Computational stylistics using artificial neural networks

The cascade-correlation architecture which is designed for classification tasks has been studied to see if it can be modified to perform function evaluation and interpolation tasks.

Function evaluation and the cascade-correlation architecture

This is the User and System Manual for the TasCas Cascade-Correlation artificial neural network simulator. The system options, data format, output and error are described, along with details relevant to the code structure and assumptions made in the development of the package. Examples are presented throughout.

TasCas ¿ a Cascade-Correlation simulator

Cascade-correlation produces networks with all possible feedforward connections. The pruning of redundant connections using a simple saliency measure is examined, along with three methods for stopping the pruning process which may be used with any network type. The methods are shown to be effective and it is further shown that a large number of redundant connections may be located and removed from cascade-correlation networks.

Pruning within cascade-correlation

Results of experiments using several variations of Cascade-Correlation are presented. The first of these examines the application of patience parameters to the addition of hidden nodes with the aim of halting network training. The other techniques involve altering standard candidate training: both training candidates in subgroups of the same node style and training candidates individually, instead of training the whole candidate pool.

Extensions to Cascade-Correlation training

A new method of benchmarking neural networks based on Voronoi diagrams is introduced. Their complexity is examined and it is shown that data sets of increasing difficulty may be generated. Experiments are conducted examining the performance of five known classification methods on examples of Voronoi data sets.

Generating data sets for benchmarking

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