EP4341876A1 - Procédé et système mis en oeuvre par ordinateur pour déterminer des paramètres de système optimisés d'un système technique à l'aide d'une fonction de coût - Google Patents

Procédé et système mis en oeuvre par ordinateur pour déterminer des paramètres de système optimisés d'un système technique à l'aide d'une fonction de coût

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Publication number
EP4341876A1
EP4341876A1 EP22728035.1A EP22728035A EP4341876A1 EP 4341876 A1 EP4341876 A1 EP 4341876A1 EP 22728035 A EP22728035 A EP 22728035A EP 4341876 A1 EP4341876 A1 EP 4341876A1
Authority
EP
European Patent Office
Prior art keywords
system parameters
cost function
statistical analysis
parameters
output values
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22728035.1A
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German (de)
English (en)
Inventor
Christian Wirth
Jörg DIETRICH
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Continental Automotive Technologies GmbH
Original Assignee
Continental Automotive Technologies GmbH
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Filing date
Publication date
Application filed by Continental Automotive Technologies GmbH filed Critical Continental Automotive Technologies GmbH
Publication of EP4341876A1 publication Critical patent/EP4341876A1/fr
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • the invention relates to a computer-implemented method for determining a cost function, with the cost function being provided for determining optimized system parameters of a technical system, with the technical system having various components that can be set by the system parameters, and with the technical system providing various output values for when the system parameters are set the various components created.
  • the invention also relates to a computer system.
  • these parameterization problems are problems in which the technical system has freely selectable parameters that influence the system behavior, with the system behavior being subject to an evaluation.
  • Approximation methods or algorithms can be used to determine suitable parameters.
  • Such problems are regarded as optimization problems, in which the parameters represent free variables and a so-called cost function, which represents an evaluation function, is to be optimized.
  • This cost function describes the quality of the selected parameters and is evaluated by testing the effect of the parameters using the system.
  • cost function In most cases there is also no scalar cost function, since the physical system provides several output values and the output values cannot be interpreted directly as costs (or benefits). Ie the expected quality is not directly one of the (numerical) output values.
  • the output of the system then forms the Enter the cost function, where the cost function is implicitly determined by the system or by experts. This means that the system has output values that can be used to show the quality of the system.
  • system parameters usually have to be determined individually for each parameterization problem using experts and expert knowledge, with a change in the model or minor changes in the technical system making complex new determinations necessary.
  • cost functions determined by experts require a clear, reliable definition that enables automatic evaluation. This is normally not possible due to only abstract expert knowledge.
  • determining a cost function is an iterative process in which the expert looks at the optimized system parameters to further adjust the cost function until the result meets his expectations.
  • WO 2001 061 573 A2 discloses a method for calculating a model of a technical system which has a function structure with functions and at least one undetermined parameter, with the steps: querying the function structure of the model; querying a data file; creating an optimization environment for computing the parameters of the model; generating seed values for the at least one undetermined parameter from the function structure; Calculate and output the parameters.
  • the object is achieved by a computer-implemented method having the features of claim 1 and a computer system having the features of claim 8.
  • the object is achieved by a computer-implemented method for determining optimized system parameters of a technical system using a cost function, with the cost function being provided for determining optimized system parameters of the technical system, with the technical system having various components that can be set by the system parameters, and with Setting the system parameters the technical system generates different output values for the different components, comprising the steps: - Determine a function space with a definition space, where the function space corresponds to a set of functions in which the cost function lies,
  • each of the probability functions indicating the probability with which the underlying rule is fulfilled by any cost function from the function space
  • the combination of all probability functions for determining the cost function can be understood to mean, for example, that the cost function is determined as a function from the function space such that the probability function is maximized when the selected function is applied to the output values.
  • the probability functions thus form the optimization goal for determining a cost function within the function space.
  • the probability functions does not become a multidimensional one Optimization goal, but understood a scalar.
  • new (cost) function parameters are optimized step by step by the computer-implemented method until they maximize the overall degree of fulfillment in relation to the expert rules.
  • the return value of the cost function should be a scalar value to enable a best solution to be determined unambiguously. Furthermore, the computer-implemented method no longer requires an expert who, after the optimization, has to select a solution from the Pareto Front, which in turn requires expert experience or expert intuition.
  • the computer-implemented method is not based on a predefined cost function, but on a large number of rules and examples that the cost function is intended to comply with.
  • the technical system is based on these rules.
  • the method generates a scalar cost function that enables autonomous optimization.
  • a selection by experts of the best result, for example from a Pareto set, is no longer necessary, ie the expert only has to define rules, not concrete target values, since the resulting cost function has a scalar return value and therefore no Pareto front is created here.
  • a function space is determined in which the desired cost function lies.
  • a function space is a set of functions that all have the same domain and that can be defined by specifying function parameters.
  • the functional space is usually infinitely dimensional.
  • the function space can be modeled automatically and/or by experts or automatically in connection with experts or by a machine learning method, or the proposed function space can be reduced as a result.
  • the usual functions in machine learning, such as kernel functions or neural networks, can be used here.
  • An expert system with expert knowledge can also be used to model the function space.
  • random system parameters are determined, which lie in the definition space. These can be automated or selected/determined by an algorithm.
  • the random system parameters are applied to the technical system and the corresponding output values are thereby determined and stored or logged.
  • the technical system is modeled by an adaptive statistical analysis method.
  • the statistical analysis method is trained by using the random system parameters as the input values and the output values as the target values.
  • the system parameters and output values can thus be used as training data for the statistical analysis method in order to predict later output values of any system parameters on the basis of the training data.
  • a such a statistical analysis method can, for example, be a regression analysis, for example a Gaussian process regressor.
  • Such a regression analysis can also determine the uncertainty of the prediction. Since not all possible combinations of system parameters can be tested, there is always some uncertainty or inaccuracy related to the model. In addition, uncertainties can arise due to random and natural fluctuations in the technical system and the lack of precise knowledge about the technical system. This means that the technical system can only be modeled with certain uncertainties and probabilities due to incomplete information.
  • the individual rules can be dependent on one another.
  • the rules can be specified, for example, in a preferred representation form, ordinal and/or numeric representation form depending on the system parameters. Examples of such rules are:
  • an expert's expectations of the behavior of the technical system can be defined.
  • Such rules can be based on a wealth of experience and/or the system can be based on limit values for the machine parameters/machine settings that the machine/the technical system should comply with during its setting (operation) (e.g. as minimum and maximum system parameters to avoid overloading or to prevent malfunctions) or output values that the technical system should comply with in the various modes of operation.
  • the individual rules can be dependent on each other.
  • the rules can be generated automatically using an expert system, for example.
  • the rules can also be provided with a weighting to express that not all rules are equally reliable.
  • the rules determined above are translated as probability functions, with each of the probability functions specifying the observed probability with which the rules are satisfied by any cost function from the function space.
  • the cost function is determined, i.e. its function parameters, by determining that cost function from the function space which maximizes the probability by applying the cost function with known output values and by maximizing the degree of fulfillment of the probability function.
  • the cost function can be optimized using an optimization method by maximizing the observed probabilities.
  • the rules can preferably, but not necessarily, always be translated as a pairwise probability function. This means that the existing rules are translated into pairwise probability functions. Here, pairs are formed for which the rule applies.
  • the probability can be designed as a sigmoid function, for example.
  • the cost function results from the resulting certain probability functions.
  • the cost function can be optimized with the aid of an optimization method. In this way, optimized or new system parameters can be found.
  • the system parameters can be determined using the now determined cost function, through which the costs of the system, given by the application of the cost function to the resulting output values, are minimized.
  • the computer-implemented method can also be used to determine system parameters without human trial and error, which can ensure a high degree of quality.
  • the computer-implemented method also allows expert knowledge to be reproduced in a comprehensible notation.
  • the rules can be based on preferences, ordinal scores, and numeric scores of the system parameters and their output values.
  • the technical system it is possible for the technical system to be mapped or simulated virtually as a simulation. This enables a simpler determination and, above all, validation of the optimized system parameters than when using the real technical system to validate determined optimized system parameters.
  • the technical system is a physical system in which the system behavior (or components of the technical system) can be changed by freely adjustable parameters (control variables).
  • control variables control variables
  • certain manipulated variables of the components of the technical system
  • mechanically, electrically or digitally which changes the measurable properties of the system. This raises the problem of the optimal manipulated variables in relation to the measurable properties.
  • One embodiment relates to the determination of optimal parameters for the operation of a control device (an ECU, electronic control unit), for example in a vehicle.
  • a control device an ECU, electronic control unit
  • the computer-implemented method includes the following steps:
  • optimal system parameters can be found by using an optimization method to determine the system parameters that minimize the most probable cost function. Through this, the parameters with the lowest costs can be identified.
  • the method includes a termination criterion. This can be at a predetermined minimum cost.
  • the statistical analysis method and the cost function each have an uncertainty.
  • the computer-implemented method includes the following steps:
  • the computer-implemented method for optimizing the uncertainty of the cost function and the uncertainty of the statistical analysis method can use active learning methods.
  • Active learning essentially means the possibility of asking for the correct outputs for some of the inputs.
  • the questions can be determined automatically / automatically, which promise a high gain of information in order to keep the number of questions as small as possible.
  • the computer-implemented method allows active learning to be used to reduce the number of parameter sets to be evaluated and thus the effort (cost and time).
  • a regression analysis is preferably used as the statistical analysis method. Relation between dependent and independent variables can be modeled by means of a regression analysis. However, the informative value of such a regression is based on the completeness of the model; the more complete the model, the better the result.
  • Such a regression can be used in particular for complex relationships.
  • a Gaussian process regression is used as the regression analysis.
  • An advantage of regression using Gaussian processes is that both the functional values and their uncertainties can be easily determined.
  • the object is also achieved by a computer system that is set up to determine optimized system parameters of a technical system using a cost function.
  • the cost function is provided for determining optimized system parameters of the technical system.
  • the technical system has various components that can be set by the system parameters, and when the system parameters are set, the technical system generates various output values for the various components.
  • the computer system has a processor which is designed to determine a function space with a definition space, the function space corresponding to a set of functions in which the cost function lies.
  • the processor is also designed to determine random system parameters that are in the definition space and to apply the random system parameters to the technical system in order to determine output values corresponding to the random system parameters.
  • the processor is also designed to model the technical system using a statistical analysis method and to train the statistical analysis method using the system parameters as input values and the output values as target values.
  • the processor is also designed to generate a plurality of rules on which the technical system is based, which are based on the various system parameters and the output values corresponding thereto, and to generate a plurality of probability functions using one or more rules.
  • Each of the probability functions indicates the probability with which the underlying rule can be satisfied by any cost function from the function space.
  • An optimization unit is provided and set up to combine all probability functions by maximizing the total probability of all rules and to optimize the system parameters given the cost function.
  • the computer system has an output unit which is designed to set the components of the technical system for the optimized system parameters.
  • the processor is designed to optimize the cost function with regard to the costs of obtaining optimized system parameters and inputting the optimized system parameters into the trained statistical analysis method and determining the new output values using the statistical analysis method.
  • the processor can be configured to abort the computer-implemented method in the event of an abort criterion, the abort criterion depending on predetermined costs.
  • the statistical analysis method may have uncertainty and the cost function may have uncertainty.
  • the processor can also be designed to optimize the cost function with regard to the uncertainties to obtain optimized system parameters and to input the system parameters optimized as a result into the trained statistical analysis method and to determine the new output values using the statistical analysis method.
  • 2 shows the method schematically using a locking system.
  • 1 shows the method for determining a cost function schematically.
  • the cost function is intended to determine optimized system parameters of a technical system.
  • the technical system has various components that can be set using the system parameters.
  • the technical system When setting the system parameters, the technical system generates different output values for the different components.
  • parameterization problems are problems in which a technical system has system parameters that can be chosen freely and that influence the system behavior, with the system behavior being subject to an evaluation.
  • the system parameters represent free variables that are optimized using a cost function (evaluation function).
  • This cost function describes the quality of the system and is evaluated by testing the effects of the system parameters using the technical system or a simulation of the system. The output of the system (output values) then forms the input of the cost function.
  • An environment for such parameterization problems are, for example, automatic locking systems for tailgates, (sliding) doors, windows or sunroofs, drive units and injection systems, transmission systems, exhaust gas regulation systems, manufacturing and production processes, printed circuit boards, temperature protection systems, etc.
  • a function space is determined with a domain of definition in which the desired cost function lies.
  • the function space can be infinitely dimensional.
  • the function space can be modeled automatically and/or by experts or automatically in connection with experts or by a machine learning method.
  • the proposed functional space by a machine learning processes are reduced.
  • the usual functions in machine learning such as kernel functions or neural networks, can be used.
  • An expert system with expert knowledge can also be used to model and reduce the functional space. This also reduces the amount of data required.
  • random system parameters that are in the definition space are determined. These can be determined automatically using an algorithm.
  • a third step S3 the selected system parameters are entered into the technical system in order to obtain corresponding output values.
  • a fourth step S4 the system parameters and the output values are now used as training data for a regression analysis, in particular a Gaussian process regression.
  • the system parameters are used as input values and the output values as target data.
  • the regression is trained to predict the output values based on the input values.
  • the uncertainty of the prediction is determined by the regressor.
  • a fifth step S5 several rules underlying the technical system are generated, which are based on the various system parameters and the corresponding output values.
  • the system parameters and the output values are thus evaluated using the rules.
  • the evaluation can be carried out, for example, by an expert system with expert knowledge.
  • the individual rules can be dependent on one another.
  • the rules can be specified, for example, in a preferred representation form, ordinal and/or numeric representation form depending on the system parameters. Examples of such rules are: - The output values of the system parameters x are very good.
  • a seventh step S7 all probability functions are combined to determine the cost function by maximizing the overall probability of all rules, i.e. the cost function is determined as a function from the function space such that the probability function is maximized when the selected function is applied to the output values.
  • the probability functions thus form the optimization goal for determining a cost function within the function space.
  • the summary of the probability functions does not mean a multidimensional optimization goal, but a scalar one.
  • the cost function is optimized in terms of probabilities given expert rules and known output values. This cost function is then suitable to optimize the system parameters.
  • step S8 optimal system parameters are generated using the determined cost function.
  • the optimized system parameters are then entered into the trained statistical analysis method and the new output values are determined by the statistical analysis method. Furthermore, based on these new output values and system parameters, new rules and probabilities can be generated.
  • This iteration can be used to find optimal system parameters by using an optimization method to determine the system parameters that minimize the most probable cost function. This allows the parameters with the lowest costs to be identified.
  • a termination criterion can also be defined, for example that the method stops at a predetermined minimum cost value.
  • the cost function is minimized with regard to the uncertainty of the cost function and the uncertainty of the regression method using methods from the field of active learning, and optimal system parameters are generated.
  • the optimized system parameters can then be entered into the trained statistical analysis method and new output values can be determined by the statistical analysis method. Furthermore, based on these new output values and system parameters, new rules and probabilities can be generated. This determines the output values/system parameters that minimize the uncertainty of the regression procedure and the uncertainty of the cost function.
  • Active learning essentially means the possibility of asking for the correct outputs for some of the inputs.
  • the questions can be determined automatically / automatically, which promise a high gain of information in order to keep the number of questions as small as possible. Therefore, active learning methods are used to determine those system parameters that account for the combined uncertainty about the Minimize the parameter space and the functional space of the cost function, so a very good result can be achieved in a short time.
  • the computer-implemented method allows active learning to be used to reduce the number of parameter sets to be evaluated and thus the effort (cost and time).
  • the generated optimized system parameters are output for setting the components of the technical system.
  • FIG. 2 describes the computer-implemented method using the example of a locking system for vehicles.
  • parameterizable systems are responsible for detecting whether an object/person is trapped and the closing process must therefore be interrupted.
  • these locking systems not only affect the detection of jamming, but also the locking behavior itself.
  • the locking behavior can be subject to explicit requirements of the vehicle manufacturer.
  • a first step A1 the function space is determined with a definition space for the system parameters. For example, it is known here that the maximum closing force must always be below a certain value. Therefore, this part of the cost function can be determined using a limit function G Y , but it is not yet determined how quickly the costs will increase if the limit is exceeded.
  • a second step A2 system parameters are selected and entered as input data into the technical system to obtain corresponding output values (third step A3).
  • a fourth step A4 the system parameters and the starting values that match them are used as training data for a regression.
  • the chosen system parameters represent the input values for the regressor, while the observed output values define the target values.
  • a Gaussian process or a Gaussian process regression can preferably be trained here, which is able to predict not only an expected value but also the uncertainty.
  • a Gaussian process essentially represents temporal, spatial or any other function whose function values can only be modeled with certain uncertainties and probabilities due to incomplete information.
  • the expected value of a random variable describes the number that the random variable assumes on average.
  • a fifth step A5 rules on which the technical system is based are automatically defined, for example, using the system parameters and their output values.
  • empirical values or operating parameters (from the manufacturer) or customer requests can be formulated as a rule.
  • the rules can be provided with a weighting to express that not all rules are equally reliable. For example, the first rule is rated higher than all others.
  • these rules can be based on preferences, ordinal ratings and numerical ratings of the system parameters/output values.
  • Probability functions generated based on a set of rules is translated, for example, as a pairwise probability function.
  • the first rule can be defined as a sigmoid function of the cost difference.
  • a sigmoid function is a mathematical function with an S-shaped graph.
  • the probability functions indicate the correctness of the rule in relation to the system parameters of the cost function.
  • the final probability function is determined from the set of all probability functions that were derived from the rules.
  • the result of the seventh step A7 is now a function that defines the probability of the correctness of the cost function as a function of the free parameters g,b, a 1 , a 2 , s.
  • Posterior sampling methods can be used to determine the probability and uncertainty of various functional parameters.
  • a posterior sampling method is, for example, the Flamiltonian Monte Carlo method.
  • Flamiltonian Monte Carlo FIMC
  • MCMC Markov Chain Monte Carlo method
  • the function parameters that have the highest probability are used to determine the potentially best system parameters. Flier this allows the cost function to be fully defined. As a result, optimized system parameters are known.
  • the cost function is optimized with regard to costs and the optimized system parameters are input into the trained Gaussian process regressor in order to predict the output values for the optimized system parameters and thus to determine their expected costs. Through this, the system parameters with the lowest costs can be identified.
  • a termination criterion can also be defined, for example that the method stops at a predetermined minimum cost value.
  • the magnitude of the combined uncertainty can be determined in an additional or alternative ninth step A9. Optimization methods from the field of active learning can be used, which attempt this uncertainty so quickly to minimize as possible. For example, the lower confidence bound (LCB) can be used to choose new system parameters. Thus, not the system parameters with the lowest expected costs are chosen, but the system parameters that minimize the uncertainty.
  • LLB lower confidence bound
  • FIG. 2 The example described in FIG. 2 is shown below again in a generalized and simplified manner, showing how the cost function is determined for a given optimization method:

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour déterminer des paramètres de système optimisés d'un système technique à l'aide d'une fonction de coût. La fonction de coût est fournie afin de déterminer des paramètres de système optimisés du système technique, et le système technique présente différents composants qui peuvent être ajustés par les paramètres du système. Le système technique génère différentes valeurs de sortie pour les différents composants lors du réglage des paramètres du système. Le procédé comprend les étapes consistant à : - déterminer un espace fonctionnel avec un espace de définition dans lequel se trouve la fonction de coût, - déterminer des paramètres de système aléatoire qui se trouvent dans l'espace de définition, - appliquer les paramètres de système aléatoire au système technique et déterminer les valeurs de sortie, modéliser le système technique à l'aide d'un procédé d'analyse statistique et entraîner le procédé d'analyse statistique, et - générer de multiples règles sur lesquelles le système technique est basé et qui sont basés sur les différents paramètres de système et les valeurs de sortie, générer des fonctions de probabilité à l'aide des règles, chaque fonction de probabilité indiquant avec quelle probabilité les règles de n'importe quelle fonction de coût sont satisfaites, - combiner toutes les fonctions de probabilité pour déterminer la fonction de coût en maximisant la probabilité totale de toutes les règles, - optimiser les paramètres du système compte tenu de la fonction de coût, et - délivrer en sortie les paramètres de système optimisés afin d'ajuster les composants du système technique.
EP22728035.1A 2021-05-19 2022-05-19 Procédé et système mis en oeuvre par ordinateur pour déterminer des paramètres de système optimisés d'un système technique à l'aide d'une fonction de coût Pending EP4341876A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021205097.2A DE102021205097A1 (de) 2021-05-19 2021-05-19 Computerimplementiertes Verfahren und System zur Bestimmung einer Kostenfunktion
PCT/DE2022/200101 WO2022242812A1 (fr) 2021-05-19 2022-05-19 Procédé et système mis en œuvre par ordinateur pour déterminer des paramètres de système optimisés d'un système technique à l'aide d'une fonction de coût

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EP (1) EP4341876A1 (fr)
CN (1) CN117321613A (fr)
DE (2) DE102021205097A1 (fr)
WO (1) WO2022242812A1 (fr)

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WO2001061573A2 (fr) 2000-02-16 2001-08-23 Siemens Aktiengesellschaft Procede et dispositif de calcul d'un modele d'un systeme technique
US9926852B2 (en) * 2015-03-03 2018-03-27 General Electric Company Methods and systems for enhancing control of power plant generating units
US10152394B2 (en) * 2016-09-27 2018-12-11 International Business Machines Corporation Data center cost optimization using predictive analytics
US11061424B2 (en) * 2017-01-12 2021-07-13 Johnson Controls Technology Company Building energy storage system with peak load contribution and stochastic cost optimization
US20200005201A1 (en) * 2018-07-02 2020-01-02 Demand Energy Networks, Inc. Stochastic economic optimization of electrical systems, and related systems, apparatuses, and methods

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