Hierarchical fuzzy neural network classification

• US 20070112695A1
• Filed: 12/29/2005
• Published: 05/17/2007
• Est. Priority Date: 12/30/2004
• Status: Abandoned Application

• Current Assignee: Mohammad Jamshidi, Yan Wang
• Original Assignee: Mohammad Jamshidi, Yan Wang
• Inventors: Wang, Yan, Jamshidi, Mohammad
• Application Number:
  US11/319,536
• Publication Number:
  US 20070112695A1
• Time in Patent Office: Days
• US Class Current: 706/2
• CPC Class Codes: G06F 18/24323 Tree-organised classifiers
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]

Abstract

A method includes receiving data representing an object to be classified into classes and applying the data to a hierarchical fuzzy neural network. The hierarchical fuzzy neural network comprises multiple fuzzy neural networks arranged in a hierarchical structure. The method also includes classifying the data using the hierarchical fuzzy neural network.

Claims

1. A method for classifying data, comprising:

receiving data representing an object to be classified into classes;

applying the data to a hierarchical fuzzy neural network, wherein the hierarchical fuzzy neural network comprises multiple fuzzy neural networks arranged in a hierarchical structure; and

classifying the data using the hierarchical fuzzy neural network.

2. The method of claim 1, wherein the data comprises multiple sets of data representing the object, each set of the multiple data sets including different information about the object.

3. The method of claim 1, further comprising:

building the hierarchical fuzzy neural network; and

training the hierarchical fuzzy neural network using training data.

4. The method of claim 3, wherein building the hierarchical fuzzy neural network comprises:

grouping the classes based on a relationship of the classes; and

arranging the fuzzy neural networks at hierarchy levels in the hierarchical fuzzy neural network based on the relationship of the classes.

5. The method of claim 4, wherein the classes are grouped using expert knowledge.

6. The method of claim 4, wherein the fuzzy neural networks are arranged using expert knowledge.

7. The method of claim 3, wherein training the hierarchical fuzzy neural network comprises:

determining the training data for the fuzzy neural networks in the hierarchical fuzzy neural network;

training the fuzzy neural networks using the training data to determine rules for the fuzzy neural networks; and

modifying the rules in the fuzzy neural networks, based on the training.

8. The method of claim 7, wherein the fuzzy neural network are trained using expert knowledge.

9. An apparatus configured to perform the method of claim 1.

10. A system for classifying data, comprising:

an input for receiving data representing an object to be classified into classes; and

a processor configured to apply the data to a hierarchical fuzzy neural network, and classify the data using the hierarchical fuzzy neural network, wherein the hierarchical fuzzy neural network comprises multiple fuzzy neural networks arranged in a hierarchical structure.

11. The system of claim 10, wherein the processor is configured to build the hierarchical fuzzy neural network, and train the hierarchical fuzzy neural network using training data.

12. The system of claim 11, wherein the processor is configured to group the classes based on a relationship of the classes and arrange the fuzzy neural networks at hierarchy levels in the hierarchical fuzzy neural network based on the relationship of the classes.

13. The system of claim 12, wherein the processor is configured to group the fuzzy neural networks using expert knowledge.

14. The system of claim 12, wherein the processor is configured to arrange the fuzzy neural networks using expert knowledge.

15. The system of claim 11, wherein the processor is configured to determine the training data for the fuzzy neural networks in the hierarchical fuzzy neural network, train the fuzzy neural networks using the training data to determine rules for the fuzzy neural networks, and modify the rules in the fuzzy neural networks based on the training.

16. The system of claim 15, wherein the processor is configured to train the fuzzy neural networks using expert knowledge.

17. A method of classifying image data, comprising:

receiving data representing an object to be classified into classes, the data comprises multiple sets of data representing the object, each set of the multiple data sets including different information about the object;

building a fuzzy neural network using expert knowledge;

applying the data to the fuzzy neural network; and

classifying the data using the fuzzy neural network.

18. The method of claim 17, wherein building the fuzzy neural network comprises:

applying training data to the fuzzy neural network; and

modifying a rule of the fuzzy neural network based on an output of the fuzzy neural network from the training data and expert knowledge.

19. The method of claim 18, wherein applying training data comprises:

applying a learning algorithm.

20. An apparatus configured to perform the method of claim 17.