Intelligent control with hierarchical stacked neural networks

• US 9,875,440 B1
• Filed: 04/20/2015
• Issued: 01/23/2018
• Est. Priority Date: 10/26/2010
• Status: Active Grant

• Current Assignee: Michael Lamport Commons
• Original Assignee: Michael Lamport Commons
• Inventors: Commons, Michael Lamport
• Primary Examiner(s): Shaawat, Mussa A
• Assistant Examiner(s): Kim, Kyung
• Application Number:
  US14/691,439
• Time in Patent Office: 1,009 Days
• Field of Search: None
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: F03D 7/046 with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G05B 2219/25255 Neural network
• CPC Class Codes: G05B 2219/41054 Using neural network techniques
• CPC Class Codes: G06N 3/00 Computing arrangements based on biological models
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 7/046 Implementation by means of a neural network neural networks using fuzzy logic G06N3/043
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

A method of processing information is provided. The method involves receiving a message; processing the message with a trained artificial neural network based processor, having at least one set of outputs which represent information in a non-arbitrary organization of actions based on an architecture of the artificial neural network based processor and the training; representing as a noise vector at least one data pattern in the message which is incompletely represented in the non-arbitrary organization of actions; analyzing the noise vector distinctly from the trained artificial neural network; searching at least one database; and generating an output in dependence on said analyzing and said searching.

Claims

1. A method of processing information, comprising:

receiving a message;

processing the message with a trained artificial hierarchical stacked neural network implemented with at least one automated processor, comprising a plurality of hierarchical layers, each respective hierarchical layer being trained according to at least one training criteria, the at least one training criteria differing for respective ones of the plurality of hierarchical layers, and comprising at least one set of outputs which together represent a non-arbitrary organization of actions based on an architecture of the respective hierarchical layer and the prior training of the respective hierarchical layer;

the message being received as an input by a respective first hierarchical layer of the trained artificial hierarchical stacked neural network;

generating a search query for at least one database external to the hierarchical stacked neural network, based on the message, the search query being generated within the hierarchical stacked neural network as a neural network layer output from a respective second hierarchical layer of the trained artificial hierarchical stacked neural network at a respectively higher hierarchical level than the respective first hierarchical level which receives the message;

communicating the search query through an automated communication channel, wherein the at least one database is configured to receive the search query from the automated communication channel and produce a database response based on an index of a plurality of database records corresponding to the search query;

receiving the database response from the at least one database through the automated communication channel, as a neural network input into a respective third hierarchical layer of the trained artificial hierarchical stacked neural network;

generating an output as a non-arbitrary organization of actions from a respective fourth hierarchical layer of the trained artificial hierarchical stacked neural network, selectively in dependence on the database response.

2. The method according to claim 1, wherein at least one respective hierarchical layer of the trained artificial hierarchical stacked neural network is trained to process information output from a respectively lower hierarchical layer according to at least a concrete developmental stage according to hierarchical complexity theory.

3. The method according to claim 2, wherein the database represents information records having an information complexity of at least an abstract developmental stage according to hierarchical complexity theory.

4. The method according to claim 1, wherein the database comprises an Internet database having records acquired by a web crawler, and the database response comprises identifications of a plurality of records corresponding to the search query.

5. The method according to claim 1, wherein the query comprises a set of words.

6. The method according to claim 5, wherein the query comprises an ordered string of words.

7. The method according to claim 1, wherein the plurality of hierarchical layers of the trained artificial hierarchical stacked neural network are trained according to hierarchical complexity theory sequential developmental stages.

8. The method according to claim 1, wherein the message comprises a human-generated input.

9. The method according to claim 1, wherein the generated output represents information characterizing a source of the message.

10. A system for processing information, comprising:

an input port receiving configured to receive at least one message; and

a trained artificial hierarchical stacked neural network, implemented with at least one automated processor, comprising a plurality of hierarchical layers, each respective hierarchical layer being trained according to at least one training criteria, the at least one training criteria differing for respective ones of the plurality of hierarchical layers, and comprising at least one set of outputs which together represent a non-arbitrary organization of actions based on an architecture of the respective hierarchical layer and a prior training of the respective hierarchical layer,

the trained artificial hierarchical stacked neural network being configured to;

receive the at least one message as an input to a respective first hierarchical layer of the hierarchical stacked neural network;

generate a search query for at least one database external to the hierarchical stacked neural network, based on the message, the search query being generated within the hierarchical stacked neural network as a neural network output at a respective second hierarchical layer of the hierarchical stacked neural network, at a respectively higher hierarchical level of the hierarchical stacked neural network than the respective first hierarchical level which receives the message as an input;

communicate the search query through an automated communication channel, wherein the at least one database is configured to receive the search query from the automated communication channel and produce a database response based on an index of a plurality of database records corresponding to the search query;

receive the database response from the at least one database through the automated communication channel, as a neural network input into a respective third hierarchical layer of the hierarchical stacked neural network; and

generate an output as a non-arbitrary organization of actions from at least one respective fourth layer of the trained artificial hierarchical stacked neural network, selectively in dependence on the received database response.

11. The system according to claim 10, wherein at least one respective hierarchical layer of the trained artificial hierarchical stacked neural network is trained to process information from a respectively lower hierarchical layer having an output according to at least a concrete developmental stage according to hierarchical complexity theory.

12. The system according to claim 10, wherein the database represents information records having an information complexity of at least an abstract developmental stage according to hierarchical complexity theory.

13. The system according to claim 10, wherein the database comprises an Internet database having records acquired by a web crawler, and the database response comprises identifications of a plurality of records corresponding to the search query.

14. The system according to claim 10, wherein the plurality of hierarchical layers of the trained artificial hierarchical stacked neural network are trained according to sequential developmental stages consistent with hierarchical complexity theory.

15. The system according to claim 14, wherein the query comprises an ordered string of words.

16. The system according to claim 10, wherein the trained artificial hierarchical stacked neural network is configured to generate the search query for at least one database from a respective hierarchical layer above respective hierarchical layer which receives the information from the at least one database.

17. The system according to claim 10, wherein the generated output represents information characterizing a cognitive nature of an author of the message.

18. A method of automatically carrying out a sensory-motor task, comprising:

receiving an input comprising environmental information generated by an automated environmental sensor;

interpreting abstract information represented in the environmental information within a hierarchical stacked artificial neural network comprising at least three separately trainable hierarchical layers, each of the at least three separately trainable hierarchical layers being trained according to at least one set of training criteria, the at least one set of training criteria differing for respective ones of the at least three separately trainable hierarchical layers;

conducting a database search of a database external to the hierarchical stacked artificial neural network comprising a plurality of records having content defined independently of the hierarchical stacked artificial neural network, based on a search query generated as an output by the hierarchical stacked artificial neural network; and

generating control information relating to a manner of carrying out the sensory-motor task by an automated controller based at least on a received result of the database search.

19. The method of claim 18, wherein the database is accessible through the Internet, and the sensory-motor task comprises driving a motor vehicle.

20. The method according to claim 18, wherein the hierarchical stacked artificial neural network comprises a plurality of hierarchical layers, respectively trained according to sequential developmental stages of hierarchical complexity theory.