Derivation of backpropagation in convolutional neural network (CNN) is conducted based on an example with two convolutional layers. The step-by-step derivation is helpful for beginners. First, the feedforward procedure is claimed, and then the backpropagation is derived based on the example. 1 Feedforward

A number of different data sets are used to compare a variety of neural network training algorithms: backpropagation, quickprop, committees of backpropagation style networks and Cascade Correlation. The results are further compared with a decision tree technique, C4.5, to assess which types of problems are more suited to the different classes of inductive learning algorithms.

Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by con...

Backpropagation (or backprop) algorithm is one of the well-known algorithms in neural networks. It is capable to deal with various types of data and also able to model a complex decision system. Some problem domains involve a large amount of data. The bigger the number of input or hidden units is, the more complex the model would be. Hence, reducing the network complexity would be an advantage ...

For many applications random access to data is critical to providing users with the level of efficiency necessary to make applications usable. It is also common to maintain data files in sequential order to allow batch processing of the data. This paper presents a method that uses a modified backpropagation neural network to locate records in a file randomly. The modifications necessary to the ...