Axial Turbine Cascade Correlation

Cascade-correlation Neural Network for Vibration Fitting of Hydraulic Turbine Units

Cascade-Correlation (CC) network is a new architecture and supervised learning algorithm for artificial neural networks. The learning algorithm of CC network and its network structure are described in this paper, the CC network with an excellent fitting ability is applied to fitting vibration characteristics of hydraulic turbine units according to different parts under three water heads. Compar...

Simplified cascade-correlation learning

Pruning within Cascade-Correlation

Cascade-Correlation produces networks with all possible feed-forward 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 ...

Knowledge-Based Cascade-Correlation

Neural network modeling typically ignores the role of knowledge in learning by starting from random weights. A new algorithm extends cascade-correlation by recruiting previously learned networks as well as single hidden units. Knowledge-based cascade-correlation (KBCC) finds, adapts, and uses its relevant knowledge to speed learning. In this paper, we describe KBCC and illustrate its performanc...

Extensions to Cascade-Correlation Training

We report on results of experiments using several variations of CascadeCorrelation. The first 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 trai...