[go: up one dir, main page]

HAYASHI et al., 1990 - Google Patents

NEURAL NETWORK DRIVEN FUZZY

HAYASHI et al., 1990

View PDF
Document ID
1171014090049485950
Author
HAYASHI I
NOMURA H
WAKAMI N
Publication year
Publication venue
Japanese Journal of Fuzzy Theory and Systems

External Links

Snippet

In the conventional fuzzy reasoning there is a tuning problem which requires slightly adjusting the inference rules so that the result of the fuzzy reasoning is same as the observed data. In other words, the shape of fuzzy numbers is determined and fuzzy …
Continue reading at www.cbii.kutc.kansai-u.ac.jp (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/0285Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks and fuzzy logic
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/02Computer systems based on specific mathematical models using fuzzy logic
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Sanner et al. Stable adaptive control of robot manipulators using “neural” networks
Bhat et al. Modeling chemical process systems via neural computation
Nauck A fuzzy perceptron as a generic model for neuro-fuzzy approaches
White Learning in artificial neural networks: A statistical perspective
Moore et al. Indirect adaptive fuzzy control
HAYASHI et al. NEURAL NETWORK DRIVEN FUZZY
Thibault et al. Neural networks in process control-a survey
Altrock et al. Multi-criteria decision making in German automotive industry using fuzzy logic
US6493691B1 (en) Assembly of interconnected computing elements, method for computer-assisted determination of a dynamics which is the base of a dynamic process, and method for computer-assisted training of an assembly of interconnected elements
Uljayev et al. Application of expert systems for measuring the humidity of bulk materials
Najim et al. Learning systems: Theory and application
JPH03134706A (en) Knowledge acquiring method for supporting operation of sewage-treatment plant
Suja Malar et al. MODELLING OF CONTINUOUS STIRRED TANK REACTOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES.
Åström Directions in intelligent control
Hu et al. A method for applying neural networks to control of nonlinear systems
Tairidis et al. Fuzzy and neuro-fuzzy control for smart structures
Zeinali et al. Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process
Hwarng Pattern recognition on Shewhart control charts using a neural network approach
Balazinski et al. Control of metal-cutting process using neural fuzzy controller
Łapa et al. Aspects of evolutionary construction of new flexible PID-fuzzy controller
de Jesus Rubio Stability Analysis of Neural Networks and Evolving Intelligent Systems
JP2564405B2 (en) Knowledge acquisition method and process operation support method
BILLAH Some Iterative Methods for Solving Fully Fuzzy Linear System of Equations
Xie et al. A new dynamic matrix control algorithm based on the FNN TS fuzzy model
Wang Nonparametric econometric modelling: A neural network approach