Artificial Neural Network
ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS IN BUSINESS Ankit Chauhan
Ist Semester – MBA (GEN)
University School of Management
Guru Gobind Singh Indraprastha University
Abstract- This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in business and organizations are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.
1. INTRODUCTION TO NEURAL NETWORKS
1.1 What is Neural Network?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, processes information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.
1.2 Historical background
Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.
Many important advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few researchers. These pioneers were able to develop convincing
Ist Semester – MBA (GEN)
University School of Management
Guru Gobind Singh Indraprastha University
Abstract- This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in business and organizations are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.
1. INTRODUCTION TO NEURAL NETWORKS
1.1 What is Neural Network?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, processes information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.
1.2 Historical background
Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.
Many important advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few researchers. These pioneers were able to develop convincing
References: 6. Neural Networks by Eric Davalo and Patrick Naim 7. Learning internal representations by error propagation by Rumelhart, Hinton and Williams (1986) 8. Klimasauskas, CC. (1989). The 1989 Neuro Computing Bibliography. Hammerstrom, D. (1986). A Connectionist/Neural Network Bibliography. 9. DARPA Neural Network Study (October, 1987-February, 1989) 10. Assimov, I (1984, 1950), Robot, Ballatine, New York. 11. Electronic Noses for Telemedicine