Fuzzy Roughness Degree Measurement Model Based on Level Effect

2014 ◽  
Vol 610 ◽  
pp. 377-380
Author(s):  
Fa Chao Li ◽  
Jian Ning Yin
Keyword(s):  
Structural Characteristics ◽  
Measurement Model ◽  
Uncertain Information ◽  
Practical Applications ◽  
Effect Function ◽  
Model Based ◽  
Decision Making System ◽  
Sets Theory ◽  
Applied Fields ◽  
Level Effect

Nowadays there are two kinds of tools which are widely used in uncertain information processing, namely, the fuzzy sets theory and fuzzy roughness sets theory. Besides, how to construct a fusion method for the two kinds of uncertain information systematically has been a focus in both academic and applied fields. In this paper, we put forward the concept of level effect function, and analyze the characteristics of a kind of level effect function. Furthermore, we present the fuzzy roughness degree measurement model based on level effect (FRD-BLE), by combining with the roughness measurement method of level cut set. All the results indicate that FRD-BLE has good structural characteristics and interpretability both in theoretical analysis and practical applications. It can be easily apply the fuzzy processing consciousness into decision-making system with the aid help of FRD-BLE.

Research on Level Effect-Based Fuzzy Prediction Method

2014 ◽  
Vol 519-520 ◽  
pp. 780-783
Author(s):  
Fachao Li ◽  
Shuo Liu
Keyword(s):  
Structural Characteristics ◽  
Prediction Method ◽  
Practical Significance ◽  
Fuzzy Decision Making ◽  
Effect Function ◽  
Fuzzy Environment ◽  
Optimal Value ◽  
Measure Model ◽  
Prediction Problems ◽  
Level Effect

As there are many prediction problems under fuzzy environments, describing the prediction results systematically and constructing a fuzzy prediction method with good structural characteristics have attracted an extensive attention. For the prediction of investment return under fuzzy environment, we first make an analysis of general fuzzy decision-making problem, and point out its limitations. Then, we discuss the association feature between decision and membership state, and give a level effect function which can describe the recognized degree under different level cut sets. Furthermore, we establish a measure model for fuzzy optimal value based on level effect function. Finally, we apply the established model to a concrete investment example, and analyze its effectiveness in fuzzy prediction. Theoretical analysis and case study show that this method has good structural characteristics and practical significance, it can enrich the existing fuzzy prediction methods to a certain degree.

A Model-Based Factored Bayesian Reinforcement Learning Approach

2014 ◽  
Vol 513-517 ◽  
pp. 1092-1095
Author(s):  
Bo Wu ◽  
Yan Peng Feng ◽  
Hong Yan Zheng
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Practical Applications ◽  
Model Based ◽  
Online Planning ◽  
Bayesian Reinforcement Learning ◽  
Bayesian Inference Method ◽  
Unknown Structure

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

scholarly journals A typology of personal factor attributes among shoppers

2014 ◽  
Vol 18 (4) ◽  
pp. 394-412 ◽  
Author(s):  
Yue Teng Wong ◽  
Syuhaily Osman ◽  
Aini Said ◽  
Laily Paim
Keyword(s):  
Hierarchical Cluster ◽  
Measurement Model ◽  
Shopping Behavior ◽  
Personal Factors ◽  
Shopping Malls ◽  
Personal Factor ◽  
Content Type ◽  
Practical Applications ◽  
Confirmatory Factor ◽  
Klang Valley

Purpose – The purpose of this paper is to derive a comprehensive model with integrated dimensions of trait constructs to understand the shoppers’ dispositional traits in consumption. This study endeavors to gain empirical validation of a motivational network of shoppers’ traits in consumption as well as to ascertain different shoppers’ typology from the configurations of personal factor attributes. Design/methodology/approach – Store-intercept method was used to collect data from a sample of 600 apparel adult shoppers at five shopping malls in Klang Valley, Malaysia. The factor structure of personal factors was achieved using confirmatory factory analysis. The hierarchical and non-hierarchical cluster analysis was employed to develop the shoppers’ typology. Findings – A relatively good fit in confirmatory factor analysis validates the applicability of the conceptualized personal factor attributes measurement model. The constitution of personal factor attributes results in three shoppers typology of Confident, Enthusiastic Shoppers; Moderate, Pragmatic Shoppers and Self-Confined, Apathetic Shoppers. Practical implications – The study provides an understanding of the personal attribute factors and disseminates insightful information about profile of shoppers’ typology. Accordingly, the implementation of the strategy which involving the personality and psychological desires of the consumers, is now possible. Originality/value – This paper stipulates new insights to discern other dimensions in personality traits to examine the personal factor attributes, by considering the elemental traits, compound traits, situational traits and surface traits in a holistic manner. The findings of this study advance the knowledge on personal factor attributes that shape shopping behavior along with practical applications.

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

Proximal policy optimization with model-based methods

2022 ◽  
pp. 1-12
Author(s):  
Shuailong Li ◽  
Wei Zhang ◽  
Huiwen Zhang ◽  
Xin Zhang ◽  
Yuquan Leng
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Transition Model ◽  
Practical Applications ◽  
Original Algorithm ◽  
Policy Performance ◽  
Model Based ◽  
Model Free ◽  
Future State ◽  
Policy Optimization

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.

Variable Precision Rough Set Model Based on Covering Relation and Uncertainty Measure

2013 ◽  
Vol 694-697 ◽  
pp. 2856-2859
Author(s):  
Mei Yun Wang ◽  
Chao Wang ◽  
Da Zeng Tian
Keyword(s):  
Rough Set ◽  
Uncertainty Measure ◽  
Probabilistic Measure ◽  
Covering Relation ◽  
Practical Applications ◽  
Variable Precision ◽  
Model Based ◽  
Equivalent Relation ◽  
Proposed Model ◽  
Variable Precision Rough Set

The variable precision probabilistic rough set model is based on equivalent relation and probabilistic measure. However, the requirements of equivalent relation and probabilistic measure are too strict to satisfy in some practical applications. In order to solve the above problem, a variable precision rough set model based on covering relation and uncertainty measure is proposed. Moreover, the upper and lower approximation operators of the proposed model are given, while the properties of the operators are discussed.

University Quality Measurement Model Based on Balanced Scorecard

2019 ◽  
pp. 131-144
Author(s):  
Thalia Obredor-Baldovino ◽  
Harold Combita-Niño ◽  
Tito J. Crissien-Borrero ◽  
Emiro De-la-Hoz-Franco ◽  
Diego Beltrán ◽  
...  
Keyword(s):  
Balanced Scorecard ◽  
Quality Measurement ◽  
Measurement Model ◽  
Model Based ◽  
University Quality
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