Academic Articles & Research Papers

Artificial Neural Networks

Neural networks for classification: a survey

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...

Multi instance neural networks

This paper is concerned with extending neural networks to multi-instance learning. In multi-instance learning, each example corresponds to a set of tuples in a single relation. Furthermore, examples are classied as positive if at least one ...

Neural network computing

At present, computers cannot think, make independent decisions, recognize visual and audible patterns, reach reliable conclusions without complete information or learn from their mistakes. The question this book and research into artificial...

Neural Networks and Pattern Recognition

Neural networks for switching

The author argues that a strong impetus for using neural networks is that they provide a framework for designing massively parallel machines. He notes that the highly interconnected architecture of switching networks suggests similarities t...

A NEURAL NETWORK PRIMER

Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...

A view of Artificial Neural Network

In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because v...

Artificial Neural Networks

Photonic Neural Networks: A Survey

Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-genera...

Artificial Neural Networks in Agriculture

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...

Slimmable Neural Networks

We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...

Pattern Recognition and Neural Networks

Artificial Neural Networks

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step...

The power of quantum neural networks

Neural networks for system identification

Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. T...

Multilayer perceptron and neural networks

Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...

Artificial neural networks enabled by nanophotonics

The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nano...

Bidirectional recurrent neural networks

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future f...

Fuzzy logic and neural network handbook

Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivi...