Page Tag-Cloud
Software Tag-Cloud
Submit Patch
Building from Source
Open Source Definition
  Popular Tags
C Plus Plus
Source Code
Notable Members
Our Company
Copyright Information
Software EULA
Pre-Release EULA
Privacy Policy
Make Contact
Downloads   0
User Rating   (Rate)
Last Updated   n/a
License   LGPL v3
Recommended Release
No recommended release for this software.

View all Releases
View Comments Subscribe to Updates Submit Bugreport

This software is open source. You can obtain the latest source code from the SVN repository or browse the releases for the source code associated with a specific release. If you make any changes which you feel improves this application, please let us know via our Contact Page.

DetermiNet is an experimental development in the field of artificial intelligence - an eventual component of a soon to be released (incomplete, to the open source community) multi-generational, evolution and machine learning project known as: AIEvolution.

DetermiNet is a C++ implementation of a multilayer perception neural network with two outside-the-box goals in mind: (1) seamless ability to merge neural network's to combine decision making capabilities and (2) prove/disprove the academic question of multi-hidden-layer benefit.

Summative Operation

^ I did this in MS paint-brush. Yea, that's masochism baby! ^

Prediction Methodology

#1 Supplying data (quasi-boolean)

A value is supplied to each of the 3 input nodes (F) and the passed in values are matched to known input states (I).
For example, if the value of "Has Children" is "Yes" then a value of 1.0 is passed from the input state node (A) to each of the input interface nodes of the hidden layer (D).

If the value of "Has Children" was passed in as "No" then the value of 1.0 is passed to the input interface nodes of the hidden layer (D) by the input state node (B) and a value of 0.0 is passed from the input state nodes (A) and (C) to the input interface nodes of the hidden layer (D).

The approach in this example is repeated for each of the three input nodes (F). If a value is not supplied or if a supplied value does not match any of the known input states (I), then a value of 1.0 will be supplied to the input interface nodes of the hidden layer (D) by the "missing state" node (H) and 0.0 will be supplied to the input interface nodes of the hidden layer (D) by all of the other nodes in the given input state cluster (I).

#2 Prediction

Some magic happens. To be continued…

#3 Results

The output node at the output node layer (J) with the highest value is the node whose value is predicted.

 Academia    AI    Experimental    Neural Network    Showcase    Source Code  

No comments currently exists for this software. Why don't you add one?
First Previous Next Last 

Login or signup to leave a comment.
Copyright © 2018 NetworkDLS.
All rights reserved.
Privacy Policy | Our Company | Contact