Neural Network World; International Journal on Neural and Mass - Parallel Computing and Information Systems

Efficient Training of Backpropagation Neural Networks

This paper focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or a separate adaptive learning rate for each weight. The learning-rate adaptation is based on descent techniques and estimates of the local constants that are obtained without additional error function and gradient evaluations. This paper proposes three algorithms to improve the different versions of backpropagation training in terms of both convergence rate and con...

Development and Investigation of a Novel Compression Technique Using Boolean Minimization

This paper suggests a new algorithm for data compression that depends on Boolean minimization of binary data. On the compressor side, the input bitstream is chopped into chunks of 16-bit each, and a "sum of products" function is found for each chunk of bits using the Quine-McClusky algorithm. The minimized "sum of products" function is stored in a file. Later, the Huffman coding is applied to this file. The obtained Huffman code is used to convert the original file into a compressed one. On t...

Toward a Generic Hybrid Neural System for Handwriting Recognition: An Application to Arabic Words

In this article, we propose an automated construction of knowledge based artificial neural networks (KBANN) for the recognition of restricted sets of handwritten words or characters. The features that better describe the chosen vocabulary are first selected, according to the characteristics of the used script, language and lexicon. Then, ideal samples of lexicon elements (words or characters) are submitted to a feature extraction module to derive their description using the chosen primitives....

Medical Image Compression by Using Vector Quantization Neural Network (Vqnn)

This paper presents a lossy compression scheme for biomedical images by using a new method. Image data compression using Vector Quantization (VQ) has received a lot of attention because of its simplicity and adaptability. VQ requires the input image to be processed as vectors or blocks of image pixels. The Finite-state vector quantization (FSVQ) is known to give better performance than the memory less vector quantization (VQ). This paper presents a novel combining technique for image compress...