Academic Articles & Research Papers

ACTIVATION FUNCTIONS IN NEURAL NETWORKS

Artificial Neural Networks are inspired from the human brain and the network of neurons present in the brain.The information is processed and passed on from one neuron to another through neuro synaptic junctions.Similarly, in artificial neu...

Fundamentals of Artificial Neural Networks

From the Publisher: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Funda...

Neural Networks

Artificial neural networks

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

Artificial Neural Network

Artificial Neural Networks

Neural Network Learning

First published in 1999, this book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. R...

Artificial neural networks

Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how...

An Introduction to Neural Networks

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain funct...

Neural networks

article Free Access Share on Neural networks: applications in industry, business and science Authors: Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , David E. Rumelhart Stanford Univ., Stanford, CA Sta...

Neural networks and their applications

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...

Neural Networks

Neural Networks

On neural networks.

JIŘÍ BENEŠTypical neural network models representing a broad survey of actual trends are considered in an unifying way as Complexes subject to specific control systems, called Formators which may have a two-level arrangement and incorporate...

Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting

Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficien...

Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter

In this paper, we propose fast Fourier transform networks for object tracking, called FFTNet. FFTNet is a correlation filter (CF)-based tracker that integrates two main components of CF, i.e., auto correlation and cross correlation between ...

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...

Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation

Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permea...

Optimization of Communication Schemes for DMA-Controlled Accelerators

The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a novel performance estimation algorithm to optimize the communic...

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...