Computational Model for Input Parameter Distribution Optimization
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Solution Overview
Problem
Conventional process model based control systems fail to simultaneously optimize input parameter distribution requirements by not addressing inter-correlations between individual input parameters during model generation and optimization.
Innovation Solution
A method and system that involves obtaining data records for input and output parameters, generating a computational model to represent interrelationships between them, determining desired statistical distributions of input parameters, and recalibrating these parameters based on those distributions to create an optimized control model for controlling engine operations in work machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional process model based control systems relate individual input parameters to control functions without addressing inter-correlations, then the control system structure remains simple, but the optimization of input parameter distribution requirements fails
Solution Approach 1:
The patent transforms the control model from a conventional individual parameter relationship to a computational model that incorporates statistical distributions and inter-correlations of multiple input parameters. This parameter transformation enables simultaneous optimization of input parameter distribution requirements while maintaining manageable system complexity through mathematical modeling techniques.
2Measurement precision
If a computational model incorporating interrelationships between input parameters is generated, then the accuracy of control optimization improves, but the complexity of model generation and calibration increases
Solution Approach 1:
The patent replaces complex manual model generation and calibration processes with automated computational methods. The system uses processors to automatically generate computational models from data records, perform statistical analysis, and recalibrate parameters, thereby reducing the practical complexity of the process while achieving high accuracy in control optimization.
3Reliability
If inter-correlations between individual input parameters are addressed during model generation, then the simultaneous optimization of input parameter distribution is achieved, but the computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-generating computational models from historical data records and pre-calculating statistical distributions and inter-correlations. This preliminary model generation and calibration allows the system to achieve reliable simultaneous optimization of input parameters without incurring excessive computational delays during actual control operations, as the heavy computational work is done in advance.
Data Source
AI summary
A method is provided for a control system. The method may include obtaining data records associated one or more input variables and one or more output parameters, and selecting one or more input parameters from the one or more input variables. The method may also include generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records, and determining desired respective statistical distributions of the one or more input parameters of the computational model.