1164 results about "Multiple Models" patented technology

A system and a method for creating a focus-exposure model of a lithography process are disclosed. The system and the method utilize calibration data along multiple dimensions of parameter variations, in particular within an exposure-defocus process window space. The system and the method provide a unified set of model parameter values that result in better accuracy and robustness of simulations at nominal process conditions, as well as the ability to predict lithographic performance at any point continuously throughout a complete process window area without a need for recalibration at different settings. With a smaller number of measurements required than the prior-art multiple-model calibration, the focus-exposure model provides more predictive and more robust model parameter values that can be used at any location in the process window.

Disclosed herein are systems and methods for demand forecasting that enable multiple-scenario comparisons and analyses by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc.) with various alternative forecast algorithm theories. The multiple model framework of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as “models”) so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as by a downstream supply planning program). Embodiments of the present invention provide a system and method whereby appropriate demand responses can be dynamically forecasted whenever given events occur, such as when a competitor lowers the price on a particular product (such as for a promotion), or when the user's company is launching new sales and marketing campaigns. Preferred embodiments of the present invention use an automatic tuning feature to assist users in determining optimal parameter settings for a given forecasting algorithm to produce the best possible forecasting model.

System and method for asynchronous distributed optimization of an enterprise. The system includes multiple computer systems coupled over a network, which store and implement multiple models, including one or more dynamic models representing respective sub-systems or processes of the enterprise. At least two of the models are interdependent. The system also includes an optimizing system that includes multiple optimizers, at least two of which are interdependent, and constraints and/or objectives, and is operable to receive information related to the enterprise from multiple information sources, and use one or more of the plurality of models to generate a solution subject to the one or more constraints and/or objectives, which is usable in managing the enterprise. Some or all of the system operates in an asynchronous manner. Various portions of the system, e.g., the models, data sources, optimizers, constraints and/or objectives, etc., may be updated, e.g., asynchronously, as desired.

Modeling systems and methods for constructing one or more models of a computing system using collected data. Certain model-based systems build topology models and/or model instances by transforming collected performance data into a collection-location independent form. In certain examples, systems include at least one agent for collecting performance data from monitored resource(s), canonical data transform (CDT) configurations, and a data transformation module for performing data transform operation(s) on the performance data based on at least one CDT configuration. The data transform operation may include generating and/or updating a topology model, assigning metrics to model object(s), updating properties of model object(s), creating associations between existing model objects, or the like. Certain systems and methods also allow for a single piece of data to be processed by multiple models or for pieces of data collected from different locations to be matched and/or associated with the same model object.

In one embodiment, a system is provided for managing a centrally managed master repository for core enterprise reference data associated with an enterprise. A centralized master repository contains the reference data, the reference data being associated with multiple schemas, each schema including one or more data models for reference data, each data model including one or more fields. A data management services framework coupled to the repository provides services for managing the reference data in the repository. The services framework supports a master schema including a union of multiple models and associated fields in the multiple schemas. The services framework also supports a thesaurus including, for each field in the master schema, a set of synonyms each representing a mapping between the field in the master schema and a corresponding field in a particular one of the multiple schemas. The master schema and thesaurus facilitate centralized management of the reference data in the repository across multiple heterogeneous external operational systems that have different associated data models and are provided indirect access to the reference data in the repository for operational use of the reference data according to associated business workflows.

Methods for three-dimensionally characterizing a reservoir while drilling a high angle or horizontal wellbore through the reservoir are disclosed. An initial reservoir model for the reservoir is selected and a section is extracted for a planned trajectory of the wellbore. A secondary model is generated by performing secondary modeling for at least part of the planned trajectory. An area of interest is identified within the secondary model where statistical uncertainty is high. Possible causes of the statistical uncertainty are identified for the area of interest within the secondary model that are not present or accounted for in the initial reservoir model. A set of parameters for the area of interest are defined at that are based on the possible causes of statistical uncertainty. The area of interest is logged with at least one logging while drilling LWD tool. Sensitivities of the LWD tool response to the subset of parameters are evaluated by performing at least one tertiary model for a range of the subset of parameters. The most sensitive parameters from the subset of parameters and corresponding measurements are identified. One or more real-time LWD measurements to be used for proactive well placement along the planned trajectory are identified and are based on the most sensitive parameters. The initial reservoir model is updated while drilling with information from the tertiary model. The model update is based on physics-based modeling or on inversion and on running multiple models and selection of a best candidate model based on correlations between the tool measurements and modeled results for each geologic model.

The invention relates to mobile tracking of vehicle targets in the communication and transportation field. A self-adapting interactive multiple model mobile target tracking method comprises the following steps of establishing mixed initialized input. including a covariance matrix of a mixed initial condition and a mixed initial state. of each model; establishing constant velocity (CV) and constant acceleration (CA) motion models; updating: calculating covariance matrix and innovation of an error according to a Kalman filter formulation; constructing a likelihood function of a target motion module by utilizing the innovation of a Kalman filter result, and calculating a Markov state transition probability matrix; carrying out estimation output after being fused by utilizing the Markov state transition probability matrix as weight of switch among each motion model. According to the self-adapting interacting multiple model mobile target tracking method disclosed by the invention, the problem that the error is increased or the tracking is failed caused by non-matching of a filter model and a target motion model due to the motion of a target in the traditional interactive multiple model algorithm is solved; the self-adapting interacting multiple model mobile target tracking method has the advantages of low calculation complexity and good tracking effect, and can be applied to target tracking of motor vehicles in the communication and transportation field.

A computer method and apparatus for adaptive model predictive control (MPC) of multivariable processes is disclosed. The adaptive MPC system can perform automatic implementation for a new MPC controller, and, for an existing MPC controller, it can perform automatic maintenance when necessary. The adaptive MPC system consists of three modules: an MPC control module, an online identification module and a control monitor module. In MPC implementation, the online identification module and the MPC control module work together and perform various steps in MPC implementation automatically and efficiently. When the MPC controller is online, the control monitor module continuously monitors the MPC performance and model quality. When control performance becomes poor and considerable model degradation is detected, monitor module will start the maintenance by activating the online identification module. The identification module will re-identify the model and replace the old model. For strongly nonlinear process units, multiple models are identified and used in control.

The invention relates to an optimal control method for a ground source heat pump refrigerating system and belongs to the field of energy-saving optimal control for air conditioning systems of buildings. Multiple models are optimally combined, as along as a prediction model contains contrasting information of the system, the prediction model can be combined with multiple good prediction models for predicting even if the prediction model is poor in effect, and a predicting character of the system can still be improved; in order to improve final predicting precision, a control system operates a combined predicting method to comprehensively utilize information provided by various methods, loss of useful information caused by a single prediction module is avoided, randomness is reduced, and predicting precision is increased. The optimal value provided by the optimal control method is ensured to meet tail end load requirements and capable of minimizing energy consumption of the system. After the optimal set value is determined, a central refrigerating system of the ground source heat pump refrigerating system is controlled to operate in an optimal set working status.

The present invention relates to the field of target tracking and more generally to a method employing improved algorithms, which achieve excellent tracking performance for a high-g maneuvering target. The two-model Interacting Multiple Model algorithm and the Interacting Acceleration Compensation algorithm will be modified by introducing adaptive factors through the detection of the normalized innovation squared which is chi-square probability distributed. The implementation results show that the modified algorithms can handle the target sudden maneuver better and are more accurate than the original algorithms.

System and method for parameterizing one or more steady-state models each having model parameters for mapping model input to model output through a stored representation of a system/process. For each model, training data representative of operation of the system/process is provided including input values and target output values. A next input value(s) and next target output value are received from the training data. The model is parameterized with the input value(s) and target output value, and derivative constraints imposed to constrain relationships between the input value(s) and a resulting model output value, using an optimizer to perform constrained optimization on the model parameters to satisfy an objective function subject to the derivative constraints. The receiving and parameterizing are performed iteratively, generating a parameterized model which is stored. Multiple models form an aggregate model of the system/process, which may be optimized to satisfy a second objective function subject to operational constraints.

Embodiments relate to generating active links between model objects. A modeling client can host modeling logic and an application programming interface (API) to create, access, manipulate, and import/export modeling objects used in modeling applications, such as engineering, medical, financial, and other modeling platforms. The source data accepted into the modeling client can include consumer or business-level applications, whose spreadsheet, database or other content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. Links can be inserted in the resulting model object to link to external resources, such as additional model objects, services, local or remote modeling tools, or other resources. The model object can share, exchange, or combine data from other model object(s), as well as instantiate functions hosted in other model object(s). Multiple links can be inserted to multiple model objects in linked list, node, or other configurations.

A system and method for ontology-enhanced traceability in design and management applications employ ontology nodes embedded in a processor executable traceability link (network) coupling processor executable requirement modules to processor executable engineering object modules to facilitate in all stages of engineering object development. The engineering object development occurs through multiple models of computation, control, and visualization platform networked together via ontology-enhanced traceability mechanism. Processor executable design rule checking module embedded in the design concept nodes creates a pathway for the development process validation and verification at early stages of the object lifecycle. Linking of ontologies/meta-models is performed for the purposes of supporting ontology-enabled traceability across multiple domains.

The invention relates to a strip steel quality forecasting, furnace condition early-warning and fault diagnosis method, in particular to a strip steel quality forecasting, furnace condition early-warning and fault diagnosis method based on partial least square, comprising the following steps: model selection: multiple models are adopted to describe the process characteristics of corresponding steel types; data preprocessing: data alignment based on the model is carried out, synchronization relation of process input and quality output is built and data dimensionless treatment is carried out to eliminate effect of process data on modeling precision owning to non-unity of physical units; an off-line model building; a PLS (partial least square) model for strip steel quality and process variable is built by utilizing a great amount of historical data in normal working conditions; determining control limit of an monitor-control index; determining variable quantity control limit; and on-line forecasting and on-line detection and fault diagnosis. In the invention, the model for the strip steel quality and the process variable is built by PLS algorithm, so as to realize real-time quality forecasting, process monitoring and fault diagnosis.