Trajectory Optimization under Changing Conditions through Evolutionary Approach and Black-Box Models with Refining

2013 ◽  
pp. 267-274
Author(s):  
Karel Macek ◽  
Jiří Rojíček ◽  
Vladimír Bičík
Keyword(s):  
Trajectory Optimization ◽  
Black Box ◽  
Evolutionary Approach ◽  
Box Models ◽  
Black Box Models

scholarly journals Trajectory Optimization for Falsification:A Case Study of Vehicle Rollover Test Generation Based on Black-box Models

2020 ◽  
Vol 53 (2) ◽  
pp. 14279-14284
Author(s):  
Sunbochen Tang ◽  
Nan Li ◽  
Ilya Kolmanovsky ◽  
Anouck Girard
Keyword(s):  
Test Generation ◽  
Trajectory Optimization ◽  
Black Box ◽  
Box Models ◽  
Vehicle Rollover ◽  
Black Box Models

Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach

Robotica ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 107-122
Author(s):  
Pranav A. Bhounsule ◽  
Ali Zamani ◽  
Jeremy Krause ◽  
Steven Farra ◽  
Jason Pusey
Keyword(s):  
Trajectory Optimization ◽  
Exponential Convergence ◽  
Initial Conditions ◽  
Control Policy ◽  
Black Box ◽  
Legged Robots ◽  
Region Of Attraction ◽  
Box Models ◽  
Highly Nonlinear ◽  
Black Box Models

SUMMARYThe popular approach of assuming a control policy and then finding the largest region of attraction (ROA) (e.g., sum-of-squares optimization) may lead to conservative estimates of the ROA, especially for highly nonlinear systems. We present a sampling-based approach that starts by assuming an ROA and then finds the necessary control policy by performing trajectory optimization on sampled initial conditions. Our method works with black-box models, produces a relatively large ROA, and ensures exponential convergence of the initial conditions to the periodic motion. We demonstrate the approach on a model of hopping and include extensive verification and robustness checks.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

Study on Individual Differences in Thermal Stress Using Black Box Models

2004 ◽  
Author(s):  
Tai S. Jang ◽  
Anthony Iyoho ◽  
S. S. Nair
Keyword(s):  
Thermal Stress ◽  
Individual Differences ◽  
Black Box ◽  
Box Models ◽  
Black Box Models

Creating Dynamic Pretrade Models: Beyond the Black Box

2018 ◽  
Keyword(s):  
Functional Form ◽  
Information Leakage ◽  
Black Box ◽  
Sensitivity Analyses ◽  
Stock Selection ◽  
Market Participants ◽  
Box Models ◽  
Portfolio Managers ◽  
Black Box Models ◽  
Shed Light

We provide a framework for investment managers to create dynamic pretrade models. The approach helps market participants shed light on vendor black-box models that often do not provide any transparency into the model’s functional form or working mechanics. In addition, this allows portfolio managers to create consensus estimates based on their own expectations, such as forecasted liquidity and volatility, and to incorporate firm proprietary alpha estimates into the solution. These techniques allow managers to reduce overdependency on any one black-box model, incorporate costs into the stock selection and portfolio optimization phase of the investment cycle, and perform “what-if” and sensitivity analyses without the risk of information leakage to any outside party or vendor.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

Transformer Modelling for Impulse Voltage Distribution and Terminal Transient Analysis

2013 ◽  
pp. 239-320
Author(s):  
Marjan Popov ◽  
Bjørn Gustavsen ◽  
Juan A. Martinez-Velasco
Keyword(s):  
Black Box ◽  
Electromagnetic Transients ◽  
Main Section ◽  
Laboratory Measurements ◽  
Voltage Distribution ◽  
Box Models ◽  
Transformer Winding ◽  
Lightning Discharges ◽  
Transient Events ◽  
Black Box Models

Voltage surges arising from transient events, such as switching operations or lightning discharges, are one of the main causes of transformer winding failure. The voltage distribution along a transformer winding depends greatly on the waveshape of the voltage applied to the winding. This distribution is not uniform in the case of steep-fronted transients since a large portion of the applied voltage is usually concentrated on the first few turns of the winding. High frequency electromagnetic transients in transformers can be studied using internal models (i.e., models for analyzing the propagation and distribution of the incident impulse along the transformer windings), and black-box models (i.e., models for analyzing the response of the transformer from its terminals and for calculating voltage transfer). This chapter presents a summary of the most common models developed for analyzing the behaviour of transformers subjected to steep-fronted waves and a description of procedures for determining the parameters to be specified in those models. The main section details some test studies based on actual transformers in which models are validated by comparing simulation results to laboratory measurements.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

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