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...

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...

Traditionally, VLSI implementations of spiking neural nets have featured large neuron counts for fixed computations or small exploratory, configurable nets. This paper presents the system architecture of a large configurable neural net system employing a dedicated mapping algorithm for projecting the targeted biology-analog nets and dynamics onto the hardware with its attendant constraints. Key...

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificiaa intelligence presents major problems as well. We claim that a promising way out of this impas...

In this paper an hybrid system and a hierarchical neural net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of non-neural clustering algorithms directly to the output of a neural net; the second one is based on a multi-layer organization of neural units. Both methods are a substantial improvement with res...

Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) speech recognizers. Both attempt to improve speech pattern discrimination while retaining the temporal processing advantages of HMMs. One approach used neural nets to provide second-stage discrimination following an HMM recognizer. On a small vocabulary task, Radial Basis Function (RBF) and back-p...

By network reliability, we mean the probability of survival of a network for a speciÞed period of time. Obtaining this probability boils down to the formulation and the evaluation of the reliability polynomial. This is a formidable task for networks with a large number of nodes, and an almost impossible task if the node life-times cascade and interact. Consequently, implementing a mathematical ...