256  Abstract: Heart is one of the vital parts of our human body, which maintains life line. The paper deals with an efficient composite method which has been developed for data compression and signal reconstruct of ECG signals. ECG data compression algorithm is needed that will reduce the amount of data to be transmitted, stored and analyzed, but without losing the clinical information conten...

The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The ...

In MLP networks with hundreds of thousands of weights which must be trained on millions of samples, the time and space complexity may become greatly large and sometimes the training of network by EBP algorithm may be impractical. Sequential Partial Updating is an effective method to reduce computational load and power consumption in implementation. This new approach is very useful for the MLP n...

In this work a new fast training algorithm for the multilayer perceptron (MLP) is proposed. This algorithm is based on optimising a criterion formed from the Mean Squared and Mean Fourth power errors, resulting in a modified form of the Standard Back-Propagation (SBP) algorithm. In this criterion, the mean fourth power error signal is appropriately weighted. The choice of the weighting paramete...

The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. T...