[go: up one dir, main page]

Schonlau et al., 1998 - Google Patents

Global versus local search in constrained optimization of computer models

Schonlau et al., 1998

Document ID
4428505368127155725
Author
Schonlau M
Welch W
Jones D
Publication year
Publication venue
Lecture notes-monograph series

External Links

Snippet

Engineering systems are now frequently optimized via computer models. The input-output relationships in these models are often highly nonlinear deterministic functions that are expensive to compute. Thus, when searching for the global optimum, it is desirable to …
Continue reading at www.jstor.org (other versions)

Classifications

    • 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
    • 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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • 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
    • 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
    • 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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric

Similar Documents

Publication Publication Date Title
Schonlau et al. Global versus local search in constrained optimization of computer models
US6725208B1 (en) Bayesian neural networks for optimization and control
US5353207A (en) Residual activation neural network
Pottmann et al. A nonlinear predictive control strategy based on radial basis function models
CN111695290B (en) Short-term runoff intelligent forecasting mixed model method suitable for changing environment
Johnson et al. Numerical solution of continuous-state dynamic programs using linear and spline interpolation
Gramacy et al. Parameter space exploration with Gaussian process trees
US6047221A (en) Method for steady-state identification based upon identified dynamics
Agarwal Combining neural and conventional paradigms for modelling, prediction and control
US20020087221A1 (en) Residual activation neural network
US6363289B1 (en) Residual activation neural network
Karjala et al. Dynamic data rectification by recurrent neural networks vs. traditional methods
Setiyoko et al. Minimax approach for semivariogram fitting in ordinary kriging
CN119414722A (en) Multi-kitchen appliance remote control method and system based on situational awareness
Petridis et al. A hybrid neural-genetic multimodel parameter estimation algorithm
Kwon et al. Comparative evaluation of adaptive and neural-network exit demand prediction for freeway control
CN114444790B (en) Method for predicting time sequence of various measuring points on gas turbine based on steady-state characteristic composition
CN119027575B (en) Kriging large model adaptive training method and system for face modeling
Schonlau et al. Constrained Optimization of Computer Models
Kárný et al. Quantification of prior information revised
Bedoui et al. Representation and control of time delay system: Multimodel approach
Lanza et al. A short-term temperature forecaster based on a novel radial basis functions neural network
Schonlau et al. Computer Models
Lughofer et al. Online adaptation of Takagi-Sugeno fuzzy inference systems
Gorinevsky Monotonic regression filters for trending deterioration faults