A Modified Error Function to Improve the Error Back-Propagation Algorithm for Multi-Layer Perceptrons

A Modified Error Function to Improve the Error Back-Propagation Algorithm for Multi-Layer Perceptrons

Minimizing Output Error in Multi-Layer Perceptrons

I. Abstract It is well-established that a multi-layer perceptron (MLP) with a single hidden layer of N neurons and an activation function bounded by zero at negative infinity and one at infinity can learn N distinct training sets with zero error. Previous work has shown that the input weights and biases for such a MLP can be chosen in an effectively arbitrary manner; however, this work makes th...

An Accelerated Error Back - Propagation Learning Algorithm

We propose a method for learning in multilayer perceptrons (MLPs). It includes new self-adapting features that make it suitable for dealing with a variety of problems without the need for parameter re-adjustments. The validity of our approach is benchmarked for two types of problems. The first benchmark is performed for the topologically complex parity problem with a number ofbinary inputs rang...

Improving Error Back Propagation Algorithm by using Cross Entropy Error Function and Adaptive Learning Rate

Improving the efficiency and convergence rate of the Multilayer Backpropagation Neural Network Algorithms is an important area of research. The last researches have witnessed an increasing attention to entropy based criteria in adaptive systems. Several principles were proposed based on the maximization or minimization of cross entropy function. One way of entropy criteria in learning systems i...

Error back-propagation algorithm for classification of imbalanced data

Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error backpropagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effect...