Data processing: artificial intelligence – Fuzzy logic hardware – Fuzzy neural network
Reexamination Certificate
1999-12-27
2002-09-03
Davis, George B. (Department: 2121)
Data processing: artificial intelligence
Fuzzy logic hardware
Fuzzy neural network
C706S006000, C706S052000
Reexamination Certificate
active
06446054
ABSTRACT:
BACKGROUND OF THE INVENTION
Fuzzy Inference Systems (FIS) and Adaptive NeuroFuzzy Inference Systems (ANFIS) exist for use in the operation of a target system. FISs and ANFISS are used in control problems and decision making problems. With control problems, the FIS or ANFIS act as a feedback control for the target system so that the inputs to the target system are properly changed based on actual system outputs. With decision making problems, the decision making rules of the target system may be “fuzzified” to provide for better decision making.
While FISs and ANFISs have been applied to many target applications, it has not been contemplated to apply an FIS or an ANFIS to the control of a user interface such as a screen display.
Over the past decade, many software programs have been developed for the display of descriptive information such as: multi-media units, web pages, graphic user interfaces (GUIs), cd-rom information, graphics, diagrams, etc. Most of these software programs are conceived to generate and display the information in a static fashion. That is, once an output is generated, the user (although he/she can manipulate the displayed information in a given framework) does not have a priori the means to change the structure and preferred way to display the available information. Consequently, and this is more true for multi-media and web-pages modules, the retrieved module may contain unnecessary (from a particular user point of view) information that makes its display cumbersome and/or lengthly and, consequently, time consuming.
Recently some commercial packages are starting to appear that provide a particular user some a priori options to display (web-pages, multi-media units) in an active/dynamic fashion the available information. However, the a priori options are few and rather limited. That is, once an option is selected the display module is generated according to some simple rules. These rules do not take into consideration a wide range of experts knowledge, nor do they display the information on the principle that for some small changes in the input, there should be some small changes in the output. Furthermore, it is very important to notice that the options to the user are currently of a crisp nature.
SUMMARY OF THE INVENTION
This invention seeks to develop an advanced FIS or ANFIS as an Intelligent Inference System which is useful for a wide variety of applications including the control of a screen display.
According to the present invention, there is provided
The development and application of an Intelligent Agent for the dynamic generation/retrieval and operation of descriptive/display software modules.
The development and application of an Intelligent Inference System for decision making/resolution cases: in particular for the design of descriptive/display software modules.
The development and implementation of an Adaptive Neuro-Fuzzy Inference System as an Intelligent Inference System.
The development and implementation of an Adaptive Fuzzy Inference System as an Intelligent Inference System.
The development and implementation of a Fuzzy Inference System as an Intelligent Inference System.
The development and implementation of a Performance Criterion for the Intelligent Inference System.
An active and dynamic input interface that allows crisp and fuzzy inputs.
A crisp and fuzzy (input) icon.
REFERENCES:
patent: 5572629 (1996-11-01), Choi
Fuzzy Rule-Based Approach for Robot Motion Control in the Presence of Obstacles, Jon Zhou and G.V.S. Raju, Division of Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, pp. 662-667, published Oct. 17, 1993.
Fuzzy Inverse Kinematic Mapping: Rule Generation, Efficiency, and Implementation, Yangsheng Xu and Michael C. Nechyba, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA, pp. 911-198, published Jul. 26, 1993.
A Fuzzy Learning Algorithm for Kinematic Control of a Robotic System, Randy A. Graca and You-Liang Gu, Dept. of Electrical and Systems Engineering Oakland University, Rochester, Michigan 48309-4401, pp. 1274-1279, published Dec. 15, 1993.
Combination of Fuzzy Logic and Neural Networks for the Intelligent Control of Micro Robotic Systems, G. Wohlke and S. Fatikow, Institute for Real-Time Computer Systems and Robotics, Faculty for Informatics, University of Karlsruhe, P.O. Box 69 80, W-7500 Karlsruhe 1, Federal Republic of Germany, pp. 691-696, published Jul. 26, 1993.
Obstacle Accommodation Motion Planning, Yansong Shan and Yoram Koren, Senior Member, IEEE, 8259 IEEE Transactions on Robotics and Automation, New York, YY, pp. 36-49, published Feb. 11, 1995.
Davis George B.
McDonnell & Boehnen Hulbert & Berghoff
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