Academic Articles & Research Papers

Neural networks in geophysical applications

Abstract Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to fin...

Neural Networks for Pattern Recognition

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural network...

An Overview of Neural Network

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...

Building neural networks

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagatio...

Bayesian Regularization of Neural Networks

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...

FPGA Implementations of Neural Networks

Essence of Neural Networks

* Introduction * Classifying Patterns * Clustering Patterns * Pattern Association * A Sample of Recurrent Networks * Some Other Network Models and A Few Practical Points * Links to Artificial Intelligence * Synthesising Symbols with Neural ...

Artificial neural networks: a tutorial

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the arti...

Binarized Neural Networks

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on a...

Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introd...

Determining the Optimum Layer Combination for Cross-Laminated Timber Panels According to Timber Strength Classes Using Artificial Neural Networks

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN)...

Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certa...

Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-M...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCN...

Surrogate Modeling of the Electric Field in the End-Winding Region of Pumped-Storage Generator Stators Based on Deep Neural Networks

The end-winding insulation structure of stator windings in pumped-storage generator units is complex, with pronounced electric field concentration under out-of-phase conditions, making them critical concerns in insulation design and conditi...

Applying Neural Networks: A Practical Guide

Techniques for building neural networks: introduction what are neural networks? how does neural computing differ from traditional programming? how are neural networks built? how do neural networks learn? what do I need to build an MLP? the ...

Neural Networks for RF and Microwave Design

From the Publisher: Discover the new, unconventional alternatives for conquering RF and microwave design and modeling problems using neural networks — information processing systems that can learn, generalize, and even allow model developm...

GPU implementation of neural networks

Neural Networks for Pattern Recognition

SPIKING NEURAL NETWORKS

Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. A...