Training a Neural Logic Network to predict financial returns: a case study

2010 ◽  
Vol 4 (1) ◽  
pp. 19 ◽  
Author(s):  
Jie Du ◽  
Roy Rada
Keyword(s):  
Financial Returns ◽  
Logic Network ◽  
Neural Logic ◽  
Neural Logic Network

A Flexible Neural Logic Network

1963 ◽  
Vol MIL-7 (2 & 3) ◽  
pp. 208-213 ◽  
Author(s):  
G. J. Dusheck ◽  
T. C. Hilinski ◽  
F. L. Putzrath
Keyword(s):  
Logic Network ◽  
Neural Logic ◽  
Neural Logic Network

scholarly journals Inductive neural logic network and the SCM algorithm

1997 ◽  
Vol 14 (2) ◽  
pp. 157-176 ◽  
Author(s):  
Ah-Hwee Tan ◽  
Loo-Nin Teow
Keyword(s):  
Logic Network ◽  
Neural Logic ◽  
Neural Logic Network

A Method of the Image Emotion Notation Based Fuzzy Reasoning

2011 ◽  
Vol 179-180 ◽  
pp. 226-232
Author(s):  
Hai Fang Li ◽  
Xiao Yan Qiao ◽  
Li Huan Men
Keyword(s):  
Fuzzy Inference ◽  
Texture Feature ◽  
Feature Space ◽  
Regions Of Interest ◽  
Eye Tracker ◽  
Space Partition ◽  
Logic Network ◽  
Data Imprecision ◽  
Neural Logic ◽  
Neural Logic Network

In this paper, we firstly get the Regions of Interest (ROI) using the Eye tracker and divide every image into two regions including ROI and Non- Regions of Interest (Non-ROI). Secondly, we extract the features of the two regions including ROI and Non-ROI, and get the whole features including texture feature and color feature. Finally, a fuzzy inference network model for image emotion notation using neural logic network is presented. The model describes how to classify the images into different emotions, and neural logic network is used to classification. The learning method proposed is multi-featured, and it allows taking into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration.

Two-valued neural logic network

1991 ◽  
Author(s):  
L. S. Hsu ◽  
K. F. Loe ◽  
Sing C. Chan ◽  
H. H. Teh
Keyword(s):  
Logic Network ◽  
Neural Logic ◽  
Neural Logic Network

NEURAL LOGIC NETWORK LEARNING USING GENETIC PROGRAMMING

2001 ◽  
Vol 01 (04) ◽  
pp. 357-368 ◽  
Author(s):  
HENRY WAI-KIT CHIA ◽  
CHEW-LIM TAN
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Back Propagation ◽  
Boolean Logic ◽  
Network Learning ◽  
Human Decision ◽  
Decision Logic ◽  
Neural Logic ◽  
A New Technique ◽  
Neural Logic Network

Neural Logic Networks or Neulonets are hybrids of neural networks and expert systems capable of representing complex human logic in decision making. Each neulonet is composed of rudimentary net rules which themselves depict a wide variety of fundamental human logic rules. An early methodology employed in neulonet learning for pattern classification involved weight adjustments during back-propagation training which ultimately rendered the net rules incomprehensible. A new technique is now developed that allows the neulonet to learn by composing the net rules using genetic programming without the need to impose weight modifications, thereby maintaining the inherent logic of the net rules. Experimental results are presented to illustrate this new and exciting capability in capturing human decision logic from examples. The extraction and analysis of human logic net rules from an evolved neulonet will be discussed. These extracted net rules will be shown to provide an alternate perspective to the greater extent of knowledge that can be expressed and discovered. Comparisons will also be made to demonstrate the added advantage of using net rules, against the use of standard boolean logic of negation, disjunction and conjunction, in the realm of evolutionary computation.

Sign in / Sign up

Export Citation Format

Share Document