A Neural Network Approach to the Validation of Simulation Models

Martens,; Pauwels, Karl; Put,

doi:10.1109/wsc.2006.323174

Cited by 4 publications

(2 citation statements)

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“…Liu Shikao [8] based on similarity theory to verify the effectiveness of the model. Martens [9][10][11] put forward the concept of similarity relation, and take neural network as the core, combine fuzzy set theory and machine learning algorithm, construct the simulation model verification method based on neural network, but it needs a large number of learning samples. This often does not exist in practical evaluation.…”

Section: Summary Of Common Credibility Evaluation Methods For Simulation Systemsmentioning

confidence: 99%

Research on Intelligent Credibility Evaluation Method of Aircraft HWIL Simulation System Based on Complex Network

Geng

2021

Lecture Notes in Electrical Engineering

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Simulation credibility is an effective means to measure the effectiveness and correctness of simulation, and it is the cornerstone of simulation. According to the uncertainty of physical effect model and the complex interaction of model information in aircraft HWIL simulation system, The idea of time-varying directed weighted network and uncertainty measurement is introduced into the process of credibility evaluation of simulation system based on complex network and credibility evaluation theory; the credibility evaluation model of time-varying topological structure based on uncertainty measurement of model information interaction is established to quantitatively evaluate the credibility of simulation system. Taking a typical HWIL simulation system as an example, the effectiveness of the credibility evaluation method of simulation system based on complex network is fully tested.

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Section: Summary Of Common Credibility Evaluation Methods For Simulation Systemsmentioning

confidence: 99%

Research on Intelligent Credibility Evaluation Method of Aircraft HWIL Simulation System Based on Complex Network

Geng

2021

Lecture Notes in Electrical Engineering

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“…In [58], a neural network approach to the validation of simulation models is presented. Specifically, a number of alternative simulation models train a neural network using multiple statistics (e.g.…”

Section: Neural Network/machine Learning Approaches For Validating Simentioning

confidence: 99%

Assertion Checking in J-Sim Simulation Models of Network Protocols

et al. 2009

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Verification and validation (V&V) is a critically important phase in the development life cycle of a simulation model. In the context of network simulation, traditional network simulators perform well in using a simulation model for evaluating the performance of a network protocol but lack the capability to check the “correctness” of the simulation model being used. To address this problem, we have extended J-Sim—an open-source component-based network simulator written entirely in Java—with a state space exploration (SSE) capability that explores the state space created by a network simulation model, up to a configurable maximum depth, in order to find an execution (if any) that violates an assertion, i.e. a property specifying an invariant that must always hold true in all states. In this paper, we elaborate on the SSE framework in J-Sim and present one of our fairly complex case studies, namely verifying the simulation model of the Ad-hoc On-demand Distance Vector (AODV) routing protocol for wireless ad-hoc networks. The SSE framework makes use of protocol-specific properties along two orthogonal dimensions: state similarity and state ranking. State similarity determines whether a state is “similar to” another in order to enable the implementation of stateful search. State ranking determines whether a state is “better than” another in order to enable the implementation of best-first search (BeFS). Specifically, we develop protocol-specific search heuristics to guide SSE towards finding assertion violations in less time. We evaluate the efficiency of our SSE framework by comparing its performance with that of a state-of-the-art model checker for Java programs, namely Java PathFinder (JPF). The results of the comparison show that the time needed to find an assertion violation by our SSE framework in J-Sim can be significantly less than that in JPF unless a substantial amount of programming effort is spent in JPF to make its performance close to that of our SSE framework.

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