Academic Articles & Research Papers

An introduction to Neural Networks

Contents Preface 9 I FUNDAMENTALS 11 1 Introduction 13 2 Fundamentals 15 2.1 A framework for distributed representation : : : : : : : : : : : : : : : : : : : : : 15 2.1.1 Processing units : : : : : : : : : : : : : : : : : : : : : : : : : : ...

Artificial neural networks

Artificial neural networks are explained, and the different types are described. Three different tasks for which they are suitable are discussed. They are pattern classification and associative memory, self-organization and feature extracti...

Memristor-based neural networks

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent p...

Evolving artificial neural networks

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations betwee...

Neural Networks for Chemists: An Introduction

From the Publisher: This book gives chemists insight into the much discussed and often not fully understood concept of neural networks. The authors pinpoint the five most widely used neural networks and learning strategies, illustrating th...

Neural Networks in Materials Science.

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach o...

Neural networks for signal processing

This collection of essays explores neural networks applications in signal and image processing, function and estimation, robotics and control, associative memories, and electrical and optical networks. Intended as a companion to Neural Netw...

neuralnet: Training of Neural Networks

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...

Protein structural model selection by combining consensus and single scoring methods.

Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) b...

ỨNG DỤNG MẠNG NƠ-RON NHÂN TẠO ĐỂ ĐIỀU KHIỂN THIẾT BỊ BẰNG GIỌNG NÓI TIẾNG VIỆT

Bài báo đề cập đến một giải pháp ứng dụng mạng nơ-ron nhân tạo (Artificial neural networks) để điều khiển thiết bị bằng giọng nói tiếng Việt. Phép biến đổi Fourier thời gian ngắn - STFT (Short time Fourier Transform) được áp dụng để trích c...

Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural ne...

Neuro-Evolution of Continuous-Time Dynamic Process Controllers

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised...

Forecasting with artificial neural networks:

Neural Networks and Brain Function

The aim of this book is to describe the types of computation that can be performed by biologically plausible neural networks, and to show how these may be implemented in different systems in the brain. The book is structured in three sectio...

Neural Network Acceptability Judgments

This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set ...

Kalman Filtering and Neural Networks

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear appr...

Forecasting with neural networks

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main feat...

Backpropagation neural networks