Neural Networks Applied to Signal Processing
Abstract
The relationship between the structure of a neural network and its ability to perform nonlinear mapping is analyzed. A new algorithm, called the conjugate gradient optimization method, for calculating the weights and thresholds of a neural network is presented. The performance of the conjugate gradient algorithm is then compared to the well known backpropagation method and shown to be more computationally efficient. A neural network using the conjugate gradient algorithm is then applied to three simple examples to demonstrate its signal processing capabilities. The first example illustrates the ability of the neural network to perform classification. The second compares the performance of a neural network predictor is shown to provide much greater accuracy than its linear counterpart. The final application presented demonstrates the ability of a neural network to perform channel equalization for a nonminimum phase channel. Its performance is then compared to its linear equivalent. Keywords: Fibonacci line search, Nonlinear signal processing, Channel equalization, Theses, Artificial intelligence, Computer architecture.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1989
- Accession Number
- ADA219605
Entities
People
- Mark D. Baehre
Organizations
- Naval Postgraduate School