Basic Methods of Fuzzy Inference and Control

2007 ◽  
pp. 137-172
Keyword(s):  
Fuzzy Inference ◽  
And Control

scholarly journals A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Sebastian-Camilo Vanegas-Ayala ◽  
Julio Barón-Velandia ◽  
Daniel-David Leal-Lara
Keyword(s):  
Predictive Control ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Clustering Techniques ◽  
Control Models ◽  
Environment Variables ◽  
Prediction And Control ◽  
Inference Systems ◽  
And Control ◽  
High Degree

Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

Integrity Assessment and Control of Offshore Topside Piping: An Expert System Based Approach

2018 ◽  
Author(s):  
R. M. Chandima Ratnayake
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Control Program ◽  
Technical Condition ◽  
Practical Significance ◽  
Illustrative Case ◽  
Inference System ◽  
Suggested Approach ◽  
Integrity Assessment ◽  
And Control

Downtime has a significant influence on the productive capability of offshore topside operating systems. Integrity assessment and control (IA&C) disciplines face major challenges in implementing a plant integrity control strategy, due to the lack of a methodology for incorporating fuzziness present in the data. To date, the employed IA&C practices face challenges in maintaining uniform quality from one integrity control program to another, due to the variability present in the technical IA&C process, especially among the different integrity assessment experts. Hence, it is vital to use expert systems-based approaches to sustain IA&C activities at an anticipated level and maintain the performance of operating assets at a target level. This manuscript provides a methodology and an illustrative case for how to perform IA&C activities for offshore topside piping. The illustrative case is demonstrated using a fuzzy inference system (FIS). Technical condition (TC) and relative degradation (RD) are selected as the inputs to the FIS for assessing the likelihood of failure (LoF). Expert system-based calculations, and how to use such results for IA&C, are demonstrated. The practical significance of the suggested approach is also discussed.

Path Planning and Control for a Flexible Transfer System

2000 ◽  
Vol 12 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Naoyuki Kubota ◽  
◽  
Yusuke Nojima ◽  
Fumio Kojima ◽  
Toshio Fukuda ◽  
...  
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Intelligent Control ◽  
Manufacturing System ◽  
Fuzzy Inference ◽  
Transfer System ◽  
Learning Automaton ◽  
Planning And Control ◽  
Manufacturing Line ◽  
And Control

We studied intelligent control of self-organizing manufacturing system (SOMS) composed of modules that self-organize based on time-series information from other modules and environment. Modules create output through interaction with other modules. We discuss intelligent control and path planning in a manufacturing line of conveyer units and machining centers. Genetic algorithm are applied to conveyor pallet path planning in global decision making and learning automaton is applied to local conveyer decision making. We use simplified fuzzy inference to control pallets providing interval, verifying its feasibility by simulation.

scholarly journals Modeling and control of an unstable system using probabilistic fuzzy inference system

2015 ◽  
Vol 25 (3) ◽  
pp. 377-396
Author(s):  
N. Sozhamadevi ◽  
S. Sathiyamoorthy
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated Approach ◽  
Fuzzy Rules ◽  
Unstable System ◽  
Inference System ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
And Control

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

scholarly journals Bacterial foraging optimization based adaptive neuro fuzzy inference system

2020 ◽  
Vol 10 (4) ◽  
pp. 3568
Author(s):  
C. Arul Murugan ◽  
G. Sureshkumaar ◽  
Nithiyananthan Kannan ◽  
Sunil Thomas
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Normal Growth ◽  
Plant Diseases ◽  
Human Beings ◽  
Bacterial Foraging Optimization ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Control

Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.

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