An introduction to neural networks: A tutorial
- Univ. of Alabama, Huntsville, AL (United States)
- Embry Riddle Aeronautical Univ., Daytona Beach, FL (United States)
Neural networks are a powerful set of mathematical techniques used for solving linear and nonlinear classification and prediction (function approximation) problems. Inspired by studies of the brain, these series and parallel combinations of simple functional units called artificial neurons have the ability to learn or be trained to solve very complex problems. Fundamental aspects of artificial neurons are discussed, including their activation functions, their combination into multilayer feedforward networks with hidden layers, and the use of bias neurons to reduce training time. The back propagation (of errors) paradigm for supervised training of feedforward networks is explained. Then, the architecture and mathematics of a Kohonen self organizing map for unsupervised learning are discussed. Two example problems are given. The first is for the application of a back propagation neural network to learn the correct response to an input vector using supervised training. The second is a classification problem using a self organizing map and unsupervised training.
- OSTI ID:
- 482047
- Report Number(s):
- CONF-960503-; TRN: 97:002904-0079
- Resource Relation:
- Conference: 1. international conference on nonlinear problems in aviation and aerospace, Daytona Beach, FL (United States), 9-11 May 1996; Other Information: PBD: 1994; Related Information: Is Part Of First international conference on nonlinear problems in aviation & aerospace; Sivasundaram, S. [ed.]; PB: 729 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Communications and control for electric power systems: Power flow classification for static security assessment
Communications and control for electric power systems: Power flow classification for static security assessment