# Accession Number:

## ADD019180

# Title:

## Neural Network Architecture for Gaussian Components of a Mixture Density Function

# Descriptive Note:

## Patent, Filed 7 Jul 95, patented 4 Aug 98,

# Corporate Author:

## DEPARTMENT OF THE NAVY WASHINGTON DC

# Personal Author(s):

# Report Date:

## 1998-08-04

# Pagination or Media Count:

## 9.0

# Abstract:

A neural network for classifying input vectors to an outcome class under the assumption that the classes are characterized by mixtures of component populations having a multivariate Gaussian likelihood distribution. The neural network comprises an input layer for receiving components of an input vector two hidden layers for generating a number of outcome class component values, and an output layer. The first hidden layer includes a number of first layer nodes each connected receive input vector components and generate a first layer output value representing the absolute value of the sum of a function of the difference between each input vector component and a threshold value. The second hidden layer includes a plurality of second layer nodes, each second layer node being connected to the first layer nodes and generating an outcome class component value representing a function related to the exponential of the negative square of a function of the sum of the first layer output values times a weighting value. The output layer includes a plurality of output nodes, each associated with an outcome class, for generating a value that represents the likelihood that the input vector belongs to that outcome class.

# Descriptors:

# Subject Categories:

- Numerical Mathematics
- Computer Systems