Academic Articles & Research Papers

Quantum Entanglement in Neural Network States

Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...

Neural networks and statistical techniques: A review of applications

Artificial Neural Networks for Misuse Detection

Misuse detection is the process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expe...

Evolutionary artificial neural networks: a review

Maximum Storage Capacity in Neural Networks

The upper storage capacity of a neural network for patterns of fixed magnetization m is calculated. The optimal capacity increases with the correlation m 2 between the patterns....

Advances in Neural Networks

Neural Networks and Fuzzy Systems

Neural Networks: A Systematic Introduction

Spin-glass models of neural networks

Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glas...

Neural Networks as Tools in Construction

The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligen...

Learning and Evolution in Neural Networks

This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learn...

Understanding of a convolutional neural network

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...

The Graph Neural Network Model

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...

Cellular neural networks

A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear circuits which process signals in real time. Like cellular automata, they are made of a massiv...

Cellular neural networks: applications

The theory of a novel class of information-processing systems, called cellular neural networks, which are capable of high-speed parallel signal processing, was presented in a previous paper (see ibid., vol.35, no.10, p.1257-72, 1988). A dyn...

Neural networks: an introduction

In this chapter, neural networks are discussed. The present field of neural networks links a number of closely related areas, such as parallel distributed processing, connectionism and neural computing, these being brought together with the...

Neural networks for wave forecasting

Neural networks — A comprehensive foundation

Neural networks for pattern recognition

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...

NEURAL NETWORK DYNAMICS

Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dy...