The case for a robust property-owning democracy

2021 ◽  
pp. 196-215
Keyword(s):  
Robust Property

An Improved Initial Alignment Method of Strap-Down Inertial Navigation System on a Swaying Base

2013 ◽  
Vol 415 ◽  
pp. 143-148
Author(s):  
Li Hua Zhu ◽  
Xiang Hong Cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Alignment Algorithm ◽  
Alignment Method ◽  
Initial Alignment ◽  
Vehicle Simulation ◽  
Robust Property ◽  
Simulation Results ◽  
Lever Arm Effect ◽  
The Impact

The design of an improved alignment method of SINS on a swaying base is presented in this paper. FIR filter is taken to decrease the impact caused by the lever arm effect. And the system also encompasses the online estimation of gyroscopes’ drift with Kalman filter in order to do the compensation, and the inertial freezing alignment algorithm which helps to resolve the attitude matrix with respect to its fast and robust property to provide the mathematical platform for the vehicle. Simulation results show that the proposed method is efficient for the initial alignment of the swaying base navigation system.

Comparative Study on Classification and Discrimination of Silken Fabrics for Skirt

2011 ◽  
Vol 175-176 ◽  
pp. 1016-1023
Author(s):  
Cai Yuan Kuang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Euclidean Distance ◽  
Visual Basic ◽  
Determination Method ◽  
Tensile Shear ◽  
Bending Properties ◽  
Robust Property ◽  
Visual Basic 6.0

Significant interrelationships between skirt shapes and properties of fabrics have been found in skirt design. In this paper, the determination method, classified fabrics based on Euclidean distance and optimum cluster can be implemented by multivariate ANOVA was proposed, after testing tensile, shear and bending properties of 30 different kinds of silken fabrics, selected as subjects, with KES. Fisher discrimination module was developed by using Visual Basic 6.0 language based on Fisher discrimination functions through SPSS 10.0. Probability neural network (PNN), established based on mechanical properties of train samples, was employed to study the classification of new sample. And the classification results were studied comparatively. The results showed that the proposed method, based on Euclidean distance and multivariate ANOVA, is feasible, and those silken fabrics can be classified into three clusters. The results also indicated that Fisher discrimination module and PNN is feasible to distinguish cluster of new sample. Discrimination of silken fabrics is easy to operate because of Fisher module, and has strong robust property in noises of test samples for the reason of PNN.

Adaptive PID Control for the Air-Conditioning System in Spray-Paint Workshop Based on Fuzzy Logic

2012 ◽  
Vol 433-440 ◽  
pp. 5733-5738
Author(s):  
Jie Dong
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Air Conditioning ◽  
Good Control ◽  
Heating System ◽  
Fuzzy Pid ◽  
Air Conditioning System ◽  
Fuzzy Pid Controller ◽  
Robust Property ◽  
Adaptive Pid Control

As the improvement of the modern workshop’s demand for cool supply, heating system, how to effectively manage the air-conditioning equipments to satisfy the production skills for air-conditioning system has become an important subject for modern workshop design. This paper comes from the No.1 motorcar company’s project about the automatic air-conditioning system of the car spray-paint workshop. For many reasons, the conventional PID control is difficult to satisfy the car spray-paint production’s strictly demands of the temperature, after a careful research of the object, we utilize the principle of fuzzy PID adaptive control to design a fuzzy PID controller, then put it into the air-conditioning heater’s controlling, through the practice, we find the controller has a good robust property and realized a good control result.

scholarly journals Journey Beyond Full Abstraction: Exploring Robust Property Preservation for Secure Compilation

2019 ◽  
Author(s):  
Carmine Abate ◽  
Roberto Blanco ◽  
Deepak Garg ◽  
Catalin Hritcu ◽  
Marco Patrignani ◽  
...  
Keyword(s):  
Full Abstraction ◽  
Robust Property ◽  
Property Preservation

scholarly journals Naturalism, tractability and the adaptive toolbox

Synthese ◽  
2019 ◽  
Author(s):  
Patricia Rich ◽  
Mark Blokpoel ◽  
Ronald de Haan ◽  
Maria Otworowska ◽  
Marieke Sweers ◽  
...  
Keyword(s):  
High Accuracy ◽  
The Other ◽  
Ecological Rationality ◽  
Adaptation Process ◽  
Robust Property ◽  
The One ◽  
Minimal Rationality ◽  
Computational Resources ◽  
Simple Heuristics ◽  
Do So

Abstract Many compelling examples have recently been provided in which people can achieve impressive epistemic success, e.g. draw highly accurate inferences, by using simple heuristics and very little information. This is possible by taking advantage of the features of the environment. The examples suggest an easy and appealing naturalization of rationality: on the one hand, people clearly can apply simple heuristics, and on the other hand, they intuitively ought do so when this brings them high accuracy at little cost.. The ‘ought-can’ principle is satisfied, and rationality is meaningfully normative. We show, however, that this naturalization program is endangered by a computational wrinkle in the adaptation process taken to be responsible for this heuristics-based (‘ecological’) rationality: for the adaptation process to guarantee even minimal rationality, it requires astronomical computational resources, making the problem intractable. We consider various plausible auxiliary assumptions in attempt to remove this obstacle, and show that they do not succeed; intractability is a robust property of adaptation. We discuss the implications of our findings for the project of naturalizing rationality.

On statistical indistinguishability of complete and incomplete market models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nikolai Dokuchaev
Keyword(s):  
Stock Prices ◽  
Incomplete Market ◽  
Market Model ◽  
Market Models ◽  
Content Type ◽  
Market Completeness ◽  
Market Incompleteness ◽  
Complete Market ◽  
Robust Property ◽  
Statistical Detection

Purpose This paper aims to investigate possibility of statistical detection of market completeness for continuous time diffusion stock market models. Design/methodology/approach The paper uses theory of forecasting to find criteria of predictability of market parameters such as volatilities and the appreciation rates. Findings It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into an incomplete one. The paper shows that market incompleteness is also non-robust: for any incomplete market from a wide class of models, there exists a complete market model with arbitrarily close paths of the stock prices and the market parameters. Originality/value The paper results lead to a counterintuitive conclusion that the incomplete markets are indistinguishable in the terms of the market statistics.

A Powerful Neural Network Method with Digital-contract Hints for Pricing Complex Options

2003 ◽  
Vol 7 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Jun Lu ◽  
◽  
Hiroshi Ohta
Keyword(s):  
Neural Network ◽  
Auxiliary Information ◽  
Financial Data ◽  
Simulation Experiments ◽  
Neural Network Method ◽  
Robust Property ◽  
Network Methods ◽  
Parallel Forms ◽  
Network Method ◽  
The Common

Many researches have proved that common neural network methods outperform parametric methods for option pricing. However, performance of the common neural network method usually suffers from the non-stationary and noisy properties of observed financial data. In this paper, we propose some parametric digital-contract (DC) hints, which can be utilized as auxiliary information to guide a neural network’s learning process about target pricing formula, and thus can be expected to get a better pricing performance in the case of observed data with noise. The DC hints are incorporated into a neural network with serial and parallel forms. Some Monte Carlo simulation experiments are performed and demonstrated that both the two forms not only have the nonparametric method’s advantages like generalization and superior accuracy, but also have the parametric method’s robust property to financial data with noise. The results also show that these two forms have their own strengths and limitations.

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