Academic Articles & Research Papers

Bayesian Learning for Neural Networks

Theory of the backpropagation neural network

The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously know...

Forecasting wind with neural networks

Cellular neural networks: theory

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

Neural Networks in Robotics

Neural networks: a tutorial

1. A Structure of an Artificial Neural Network. 2. Applications of Neural Networks. 3. The Early Years. 4. The Hopfield Net. 5. Self Organizing Maps. 6. Backpropagation. 7. Learning Laws and Continued Taxonomy. 8. Adaptive Resonance Theory....

Modular learning in neural networks

In the development of large-scale knowledge networks much recent progress has been inspired by connections to neurobiology. An important component of any neural network is an accompanying learning algorithm. Such an algorithm, to be biologi...

Artificial Neural Networks: An Overview

Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by ...

Foundations of neural networks

Understanding Neural Networks and Fuzzy Logic

Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutor...

Rule extraction: Using neural networks or for neural networks?

Digital neural networks

Neural Networks for Pattern Recognition

Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes technique...

Chaotic neural networks

Introduction to artificial neural networks

A general introduction to the subject of artificial neural networks is given and the tenuous relationship of neural networks to the biological neuron structure of the brain is also briefly outlined. The development of artificial neural netw...

Fundamentals of neural networks: architectures, algorithms, and applications

1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...

Fundamentals of Neural Networks

The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an ...

Discovering Physical Concepts with Neural Networks

Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture afte...

Pattern Recognition and Neural Networks

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously...

Neural Networks in Bioprocessing and Chemical Engineering