Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, ...

The Hopfield model neural net has attracted much recent attention. One use of the Hopfield net is as a highly parallel content-addressable memory, where retrieval is possible although the input is corrupted by noise. For binary input patterns, an alternate approach is to compute Hamming distances between the input pattern and each of the stored patterns and retrieve that stored pattern with min...

A major problem in artificial brain building is the automatic construction and training of multi-module systems of neural networks. For example, consider a biological human brain, which has millions of neural nets. If an artificial brain is to have similar complexity, it is unrealistic to require that the training data set for each neural net must be specified explicitly by a human, or that int...

We present a new algorithm for topological feature mapping. It is compared to other known feature mapping algorithms like that proposed by Kohonen or Bertsch. The main difference to known algorithms is the inversion of the learning step. This makes it possible that a neural net can be seen as a cellular automaton. A neurone learns from its neighbours instead of teaching them the new value they ...

The continuously growing of cellular net complexity, that followed the introduction of UMTS technology, has reduced the usefulness of traditional design tools, making them quite unworthy. The purpose of this paper is to illustrate a design tool for UMTS optimized net planning based on genetic algorithms. In particular, some utilities for 3G net designers, useful to respect important aspects (su...

Artificial neural networks are increasingly popular in today’s business fields. They have been hailed as the greatest technological advance since the invention of transistors. The purpose of this paper is to answer hvo of the inost frequently asked questions: “What are neural networks?” “ Why are they so popular in today’s business fields?” The paper reviews the common characteristics of neural...