A Collaborative Optimization Method for Back Analysis of Numerical Calculation Model Parameters in Rockburst Process

Abstract

The invention discloses a collaborative optimization method applicable to parameter back analysis of a high calculation cost numerical calculation model. To solve a problem that parameters in numerical simulation with high calculation cost are hard to optimize, the method comprises the following steps: by taking the difference of a physical quantity which can be monitored in practice and a physical quantity calculation value corresponding to a numerical calculation module as a target function, combining excellent small sample learning ability of an information vector machine (IVM) with excellent global optimization ability of a backtrack search algorithm (BSA), thereby rapidly obtaining parameters which relatively meet the practice. The practical calculation case shows that the parameters obtained by using the method disclosed by the invention within the same time relatively well meet the practice when compared with those of a random global optimization algorithm. The method disclosed by the invention is relatively good in applicability to parameter optimization with high calculation cost, and has the advantages of high efficiency, rapidness, simplicity and practicability.

Description

technical field [0001] The invention belongs to the application field of artificial intelligence algorithms, and relates to a collaborative optimization method for reverse analysis of numerical calculation model parameters in rockburst process, specifically, relates to a collaborative optimization algorithm based on information vector machine-backtracking search (IVM-BSA) Collaborative optimization method for back analysis of high computational cost numerical calculation model parameters. Background technique [0002] Numerical simulation is a very effective research method for many engineering problems, physical problems, and even the research of various problems in nature and human society. Numerical simulation must first establish a mathematical model that reflects the nature of the problem (such as engineering problems, physical problems, etc.). Specifically, it is necessary to establish equations (usually differential equations) that reflect the relationship between va...

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Problems solved by technology

However, both artificial neural networks and support vector machines have some open problems. For example, artificial neural networks have problems such as the difficulty of determining the optimal network topology and optimal hyperparameters, the risk of over (under) learning, and poor generalization ability of small samples. ; There is no feasible theoretical solution method for the kernel function and reasonable hyperparameters of the support vector machine, and it is difficult to guarantee the reliability of the prediction

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