Bontempi et al., 1998 - Google Patents
Recursive lazy learning for modeling and controlBontempi et al., 1998
View PDF- Document ID
- 222570304550661273
- Author
- Bontempi G
- Birattari M
- Bersini H
- Publication year
- Publication venue
- European conference on machine learning
External Links
Snippet
This paper presents a local method for modeling and control of non-linear dynamical systems from input-output data. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear …
- 238000000034 method 0 abstract description 50
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive 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
- G05B13/027—Adaptive 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rätz et al. | Automated data-driven modeling of building energy systems via machine learning algorithms | |
Cremer et al. | From optimization-based machine learning to interpretable security rules for operation | |
Petković et al. | Feature ranking for multi-target regression | |
Aladag et al. | A new time invariant fuzzy time series forecasting method based on particle swarm optimization | |
Bontempi et al. | Recursive lazy learning for modeling and control | |
Castellano et al. | Variable selection using neural-network models | |
Ghoshal et al. | Application of GA based optimal integral gains in fuzzy based active power-frequency control of non-reheat and reheat thermal generating systems | |
Bontempi et al. | The local paradigm for modeling and control: from neuro-fuzzy to lazy learning | |
Yazdani et al. | Reliability estimation using an integrated support vector regression–variable neighborhood search model | |
Evsukoff et al. | Structure identification and parameter optimization for non-linear fuzzy modeling | |
Perfilieva et al. | System of fuzzy relation equations as a continuous model of IF–THEN rules | |
Cocianu et al. | An artificial neural network for data forecasting purposes | |
Eftekhari et al. | Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter | |
Mueller et al. | GMDH algorithms for complex systems modelling | |
Shirazi et al. | EDA++: Estimation of distribution algorithms with feasibility conserving mechanisms for constrained continuous optimization | |
Assaleh et al. | Modeling nonlinear behavior of Buckling-Restrained Braces via different artificial intelligence methods | |
Kuklev et al. | Robust adaptive bayesian optimization | |
Sabes et al. | Reinforcement learning by probability matching | |
Sánchez et al. | Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms | |
Hametner et al. | Neuro-fuzzy modelling using a logistic discriminant tree | |
Akbari et al. | Similarity-based error prediction approach for real-time inflow forecasting | |
Masegosa et al. | A centralised cooperative strategy for continuous optimisation: The influence of cooperation in performance and behaviour | |
Kwiatkowski et al. | Ugae: A novel approach to non-exponential discounting | |
Chun et al. | Impact of momentum bias on forecasting through knowledge discovery techniques in the foreign exchange market | |
Cobb | Influence diagrams with continuous decision variables and non-Gaussian uncertainties |