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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Bhattacharjee, Tanuka Mallela, Jhansi Belur, Yamini Atchayarcmf, Nalini Yadav, Ravi Reddy, Pradeep Gope, Dipanjan Ghosh, Prasanta Kumar Indian Inst Sci EE Dept Bengaluru 560012 India Indian Inst Sci ECE Dept Bengaluru 560012 India Natl Inst Mental Hlth & Neurosci Dept Speech Pathol & Audiol Bengaluru 560029 India Natl Inst Mental Hlth & Neurosci Dept Neurol Bengaluru 560029 India

ISBN: (纸本)9781728176055

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson's disease (PD) impacts both articulation and prosody in an individual's speech. Complex deep neural networks exploit these cues for detection of ALS and PD. These are typically done using recordings in laboratory condition. This study aims to examine the robustness of these cues against background noise and model complexity, which has not been investigated before. We perform classification experiments with pitch and Mel-frequency cepstral coefficients (MFCC) using models of three different complexities and additive white Gaussian noise in four signal-to-noise-ratio (SNR) conditions. The findings are as follows: 1) In clean condition, pitch performs similar to MFCC across most model complexities considered, suggesting that one-dimensional pitch pattern provides discriminative cues for the classification to an extent equal to that of multi-dimensional MFCC, 2) Similar trend is observed in noisy cases when classifiers are trained and tested in matched noise and SNR conditions, 3) When the classifiers trained on clean data are applied in noisy cases, pitch based average classification accuracies are found to be 20.09% and 24.73% higher than those using MFCC for ALS vs. healthy and PD vs. healthy, respectively, suggesting robustness of pitch based classifier against noise and model complexity.

关键词： Amyotrophic Lateral Sclerosis Parkinson's disease Pitch Mel-frequency cepstral coefficients model complexity Noise

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Finite Segment model complexity of an Euler-Bernoulli Beam 8

Finite Segment Model Complexity of an Euler-Bernoulli Beam

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8th Vienna International Conference on Mathematical modelling

作者： Louca, Loucas S. Univ Cyprus Dept Mech & Mfg Engn CY-1678 Nicosia Cyprus

A common approach for modeling the dynamic behavior of distributed parameter systems is the approximation through finite-segment models. These models are able to accurately predict the dynamic behavior of the system given that "adequate" segments are included in the model. Frequency-based methodologies can be used to address the complexity of such models. The purpose of the current work is to address the complexity of distributed parameter using the previously developed activity metric. More specifically the complexity of an Euler-Bernoulli beam model is considered. Bond graph models of this system already exist in the literature and the objective is to identify the necessary complexity (number of segments). A new modeling procedure is proposed for this type of systems where the model starts from simple and the number of segments is increased until an activity based criterion is satisfied. An illustrative example is provided to demonstrate the effectiveness of this methodology. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： model complexity Energy-based modeling Metric Bond Graphs Finite Segment model

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Neural Spatial Interaction models: Network Training, model complexity and Generalization Performance

Neural Spatial Interaction Models: Network Training, Model C...

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13th International Conference on Computational Science and Its Applications (ICCSA)

作者： Fischer, Manfred M. Vienna Univ Econ & Business Vienna Austria

ISBN: (纸本)9783642396496

Spatial interaction models approximate mean interaction frequencies between origin and destination locations by using origin-specific, destination-specific and spatial separation information. The focus is on models that are based on the theory of feedforward neural networks. This contribution considers the functional form of neural spatial interaction models, including the specification of the activation functions, and discusses the problem of network training within a maximum likelihood framework that involves the solution of a non-linear optimization problem. This requires the evaluation of the log-likelihood function with respect to the network parameters. Overfitting is a problem that is likely to occur in neural spatial interaction models. To avoid this problem the contribution recommends controlling the model complexity either by regularization or by early stopping in network training. A bootstrapping pairs approach with replacement may be adopted to evaluate the generalization performance of the models.

关键词： Neural spatial interaction models network training gradient based search model complexity regularization bootstrapping generalization performance

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Quantifying model complexity via Functional Decomposition for Better Post-hoc Interpretability 19th

Quantifying Model Complexity via Functional Decomposition fo...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Molnar, Christoph Casalicchio, Giuseppe Bischl, Bernd Ludwig Maximilians Univ Munchen Dept Stat Ludwigstr 33 D-80539 Munich Germany

ISBN: (纸本)9783030438234;9783030438227

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity.

关键词： model complexity Interpretable machine learning Explainable AI Accumulated Local Effects Multi-objective optimization

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Measuring Clustering model complexity 26th

Measuring Clustering Model Complexity

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26th International Conference on Artificial Neural Networks (ICANN)

作者： Rovetta, Stefano Masulli, Francesco Cabri, Alberto Univ Genoa DIBRIS Via Dodecaneso 35 I-16146 Genoa Italy

ISBN: (纸本)9783319686127;9783319686110

The capacity of a clustering model can be defined as the ability to represent complex spatial data distributions. We introduce a method to quantify the capacity of an approximate spectral clustering model based on the eigenspectrum of the similarity matrix, providing the ability to measure capacity in a direct way and to estimate the most suitable model parameters. The method is tested on simple datasets and applied to a forged banknote classification problem.

关键词： Spectral clustering model complexity model selection

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Effect of model complexity for Estimation of Distribution Algorithm in NK Landscapes

Effect of Model Complexity for Estimation of Distribution Al...

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IEEE Symposium on Foundations of Computational Intelligence (FOCI)

作者： Liaw, Rung-Tzuo Ting, Chuan-Kang Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 621 Taiwan

ISBN: (纸本)9781467359016

Evolutionary algorithms (EAs) have been widely proved to be effective in solving complex problems. Estimation of distribution algorithm (EDA) is an emerging EA, which manipulates probability models instead of genes for evolution EDA creates probability models based on the promising solution in the population and generates offspring by sampling from these models. The model complexity is a key factor in the performance of EDA. Complex models can express the relations among variables more accurately than simple models. However, for some problems with strong interaction among variables, building a model for all the relations becomes unrealistic and impractical due to its high computational cost and requirement for a large population size. This study aims to understand the behaviors of EDAs with different model complexities in NK landscapes. Specifically, this study compares the solution quality and convergence speed of univariate marginal distribution algorithm (UMDA), bivariate marginal distribution algorithm (BMDA), and estimation of Bayesian network (EBNA) in the NK landscapes with different parameter settings. The comparative results reveal that high complexity does not imply high performance: Simple model such as UMDA and BMDA can outperform complex mode like EBNA on the tested NK landscape problems. The results also show that BMDA achieves a stable high probability of generating the best solution and satisfactory solution quality;by contrast, the probability for EBNA drastically declines after some generations.

关键词： Estimation of distribution algorithm model complexity NK landscapes probability model

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Evaluation of model complexity in model predictive control within an exercise-enabled artificial pancreas

Evaluation of model complexity in model predictive control w...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Resalat, Navid El Youssef, Joseph Reddy, Ravi Jacobs, Peter G. Oregon Hlth & Sci Univ Dept Biomed Engn 3303 SW Bond Ave Portland OR 97239 USA Oregon Hlth & Sci Univ Harold Schnitzer Diabet Ctr Portland OR 97239 USA

model predictive control (MPC) algorithms have been used often within artificial pancreas control systems both in-silico and in clinical studies. Increasingly complex models in the controller can more accurately predict the glycemic response, but they introduce increased computational complexity which can be challenging to implement especially within an embedded environment where computational resources are limited. Less complex models are also preferable in that they can be evaluated in silico against more complex plant models. There has not yet been an evaluation of how the complexity of models used within an MPC impacts performance within an artificial pancreas. A model within an artificial pancreas MPC algorithm should be as complex as necessary to accurately predict a glycemic response to meals, exercise, stress, and other disturbances, but not overly complex. In this paper, we evaluate four glucoregulatory models used within an MPC, starting with a 4-state model and increasing in complexity up to six states. We evaluate the complexity using an in-silico population derived from a more complex glucoregulatory model (9 state variables). We assess how complexity of the model impacts performance both in terms of standard control metrics such as settling time and overshoot as well as clinically relevant metrics such as percent time in euglycemia (glucose between 70 and 180 mg/dl), percent time in hypoglycemia (<70 mg/dl) and percent time in hyperglycemia (>180 mg/dl). We find that model complexity matters far less than how well the model parameters match the individual subjects. When the simplest model is used, but fit to an individual subject's data, it performed comparably with more complex models. We selected a middle-complexity model and integrated it into our previously published exercise-enabled MPC model and evaluated it in a virtual patient population both with and without the exercise model present. We found that increasing complexity by modeling exercise i

关键词： Type-1 diabetes Artificial Pancreas model Predictive Control model complexity Glucoregulatory model

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model complexity of heat pump systems to investigate the building energy flexibility and guidelines for model implementation

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APPLIED ENERGY 2019年 255卷 113847-000页

作者： Clauss, John Georges, Laurent Norwegian Univ Sci & Technol Dept Energy & Proc Engn Kolbjorn Hejes Vei 1a N-7034 Trondheim Norway

Building performance simulation (BPS) is a powerful tool for engineers working in building design and heating, ventilation and air-conditioning. Many case studies using BPS investigate the potential of demand response (DR) measures with heat pumps. However, the models are often simplified for the components of the heat pump system (i.e. heat pump, electric auxiliary heater and storage tank) and for their interactions. These simplifications may lead to significant differences in terms of DR performance so that more comprehensive models for a heat pump system may be necessary. The contribution of this work is twofold. Firstly, this work investigates the influence of the modeling complexity of the heat pump control on different key performance indicators for the energy efficiency, the DR potential and the heat pump operation. To this end, the performance of six different heat pump controls is compared. Secondly, it describes the implementation of a comprehensive control for a heat pump system in BPS tools. This control is not often documented in the BPS literature and is error-prone. Generic pseudo-codes are provided, whereas IDA ICE is taken as an example in the case study. A predictive rule-based control is implemented to study price-based DR of residential heating. It is shown that a realistic operation of the heat pump system can be achieved using the proposed modeling approach. The results prove that the modeling complexity of the system control has a significant impact on the performance indicators, meaning that this aspect should not be overlooked. For some performance indicators, e.g. the annual energy use for heating and average water tank temperature, it is shown that a proportional (P-) and proportional-integral (PI-) control can lead to similar results. If the heat pump operation is investigated in detail and a short-time resolution is required, the difference between P- and PI-controls and their tuning is important. As long as the heat pump operation and e

关键词： Heat pump system System modeling Demand response Energy flexibility model complexity

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Quantifying prior model complexity for subsurface reservoir models

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 227卷

作者： Mioratina, Tanteliniaina N. Oliver, Dean S. NORCE Norwegian Res Ctr Bergen Norway Univ Bergen Bergen Norway

In Bayesian approaches to history matching for subsurface inference, the prior model specifies the uncertain model parameters and the joint probability of those parameters before incorporating production-related data. A good prior model is generally complex enough to capture the future reservoir behavior in the long term, realistic enough to be plausible, consistent with geologic knowledge, and simple enough to allow calibration for data matching. model complexity is often associated with the number of model parameters, thus the focus on finding the sufficient number of parameters needed for history matching and quantifying future *** work explores model choice based on concepts of complexity and informativeness of models for subsurface reservoir models. It focuses on the effect of the misspecification of prior models for assimilating flow data and their predictive accuracy. The concept of the effective number of parameters is used to investigate the suitability of various types of prior models with different levels of complexity, ranging from a highly simplified polynomial trend model to a more realistic multipoint statistical model(MPS) and a family of isotropic Gaussian models and explore the effect of level of model complexity on the robustness of forecasting. The numerical experiments were performed with different combinations of data type, prior informativeness, forecast type, and model type to compare the effect of different prior models on the robustness of the results. The effective number of parameters was computed for each prior model and their accuracy for predicting future reservoir behavior was *** results suggest that effective model dimension is a useful measure of model complexity for history matching problems, although it is not independent of the data used for model calibration and the number of effective model parameters is generally much smaller than the number of model parameters. In a data-rich problem, realism of a model

关键词： model complexity model predictability Predictive accuracy model selection Prior model History matching

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Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective

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WATER RESEARCH 2020年 182卷 115973-115973页

作者： Brunetti, Giuseppe Papagrigoriou, Ioannis-Aristotelis Stumpp, Christine Univ Nat Resources & Life Sci Inst Soil Phys & Rural Water Management A-1190 Vienna Austria

Green Roofs (GRs) have proven to be a sustainable solution to stormwater management in urban areas. To boost their adoption at the large scale, there is a need to develop numerical models, which are accurate, computationally cheap, and as complex as needed to reproduce the hydrological behavior of GRs. Alternative conceptual and mechanistic approaches have been proposed and tested, however the most appropriate level of model complexity for GRs' analysis is still unknown. To cover this scientific gap, we provide a Bayesian comprehensive perspective of GR hydrological modeling, which includes a statistically rigorous Bayesian comparison of one conceptual and multiple Richards-based mechanistic GR models, and a probabilistic assessment of the information content of different observations. The analysis of the marginal likelihoods reveals that the conceptual and the unimodal van Genuchten - Mualem models are the most appropriate parameterizations, and that further layers of model complexity are not fully supported by the measurements. In addition to that, the estimated Kullback-Leibler divergences suggest that the measured volumetric water content outperforms the measured subsurface outflow and tracer concentrations in terms of informativeness, leading to the lowest model predictive uncertainty for the simulation of water fluxes. The findings of this study represent a first step to clarify the role of model complexity in GRs' analysis, and open new perspective on GRs' model-based experimental design. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Green roof model complexity Bayesian inference Experimental design

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