scholarly journals Fuzzy Rating and Membership Function Identification Methods

1998 ◽  
Vol 10 (2) ◽  
pp. 184-192 ◽  
Author(s):  
Ayumi YOSHIKAWA
Keyword(s):  
Membership Function ◽  
Identification Methods ◽  
Function Identification

Evaluating the Bottom-Up Method for Functional Decomposition in Product Dissection Tasks

2014 ◽  
Author(s):  
Joran W. Booth ◽  
Abihnav K. Bhasin ◽  
Tahira Reid ◽  
Karthik Ramani
Keyword(s):  
Prior Work ◽  
Top Down ◽  
Specific Design ◽  
Bottom Up ◽  
Functional Decomposition ◽  
Identification Methods ◽  
Future Studies ◽  
Statistical Support ◽  
Prior Literature ◽  
Function Identification

The purpose of this study is to continue to explore which function identification methods work best for specific design tasks. Prior literature describes the top-down and bottom-up approaches as equivalent methods for functional decomposition. Building on our prior work, this study tests the bottom-up method against the top-down and enumeration methods. We used a 3-factor within-subject study (n=136). While most of our diagram-oriented metrics were not statistically different, we found statistical support that: 1.) students reported that the dissection activity was more useful when using bottom-up, and 2.) that student engineers committed many more syntax errors when using the bottom-up method (by listing parts instead of functions). We believe that both these results are due to the increased focus on individual parts. We do not know if an increased attention to the parts would increase novelty or fixation, and recommend future studies to find out.

scholarly journals Comparing Functional Analysis Methods for Product Dissection Tasks

2015 ◽  
Vol 137 (8) ◽  
Author(s):  
Joran W. Booth ◽  
Tahira N. Reid ◽  
Claudia Eckert ◽  
Karthik Ramani
Keyword(s):  
Functional Analysis ◽  
Energy Flow ◽  
Prior Experience ◽  
Qualitative Difference ◽  
Top Down ◽  
Identification Methods ◽  
Future Studies ◽  
Core Concepts ◽  
Power Drill ◽  
Function Identification

The purpose of this study is to begin to explore which function identification methods work best for specific tasks. We use a three-level within-subject study (n = 78) to compare three strategies for identifying functions: energy-flow, top-down, and enumeration. These are tested in a product dissection task with student engineers who have minimal prior experience. Participants were asked to dissect a hair dryer, power drill, and toy dart gun and generate function trees to describe how these work. The function trees were evaluated with several metrics including the total number of functions generated, the number of syntactical errors, and the number of unique (relevant and nonredundant) functions. We found no statistical, practical, or qualitative difference between the trees produced by each method. This suggests that the cognitive load for this task for novices is high enough to obscure any real differences between methods. We also found some generalized findings through surveys that the most difficult aspects of using functional decomposition include identifying functions, choosing function verbs, and drawing the diagram. Together, this may also mean that for novice engineers, the method does not matter as much as core concepts such as identifying functions and structuring function diagrams. This also indicates that any function identification method may be used as a baseline for comparison between novices in future studies.

scholarly journals Two Identification Methods for a Nonlinear Membership Function

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuejiang Ji ◽  
Lixin Lv
Keyword(s):  
Parameter Identification ◽  
Iterative Algorithm ◽  
Membership Function ◽  
Nonlinear Function ◽  
Gradient Algorithm ◽  
Unknown Parameters ◽  
Identification Methods ◽  
Stochastic Gradient Algorithm ◽  
Gradient Based ◽  
Two Parameter

This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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