Structure and control of the L-LILOG Inference system

1990 ◽  
pp. 165-182 ◽  
Author(s):  
K. H. Bläsius ◽  
C. -R. Rollinger ◽  
J. -H. Siekmann
Keyword(s):  
Inference System ◽  
And Control

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.

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.

scholarly journals INTELLIGENT SYSTEM FOR THE TWO-PHASE FLOWS DIAGNOSIS AND CONTROL ON THE BASIS OF RAW 3D ECT DATA

2017 ◽  
Vol 7 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Paweł Fiderek ◽  
Tomasz Jaworski ◽  
Robert Banasiak ◽  
Jacek Nowakowski ◽  
Jacek Kucharski ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Intelligent System ◽  
Fuzzy Classification ◽  
Control Panel ◽  
Random Errors ◽  
Two Phase Flows ◽  
Two Phase ◽  
Control Module ◽  
Inference System ◽  
And Control

In this paper the new intelligent system for two-phase flows diagnosis and control is presented. The authors developed a fuzzy inference system for two phase flows recognition based on the raw 3D ECT data statistical analysis and fuzzy classification which identify the flow structure in real-time mode. The non-invasive three-dimensional monitoring is possible to conduct even in non-transparent and non-accessible parts of the pipeline. Presented system is also equipped with the two phase gas-liquid flows installation control module based on fuzzy inference which includes the feedback information from the recognition module. The intelligent control module working in a feed-back loop keep the sets of required flow regime. Presented in this paper fuzzy algorithms allow to recognize the two phase processes similar to the human expert and to control the process in the same, very intuitively way. Using of the artificial intelligence in the industrial applications allows to avoid any random errors as well as breakdowns and human mistakes suffer from lack of objectivity. An additional feature of the system is a universal multi-touched monitoring-control panel which is an alternative for commercial solution and gives the opportunity to build user own virtual model of the flow rig to efficiently monitor and control the process.

scholarly journals A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

2021 ◽  
Author(s):  
Najmeh Fatahi Nafchi ◽  
Adeleh Asemi ◽  
Hamid Tahaei
Keyword(s):  
Expert System ◽  
Delphi Technique ◽  
Rule Base ◽  
Fuzzy Expert System ◽  
Weed Detection ◽  
Inference System ◽  
Statistical Population ◽  
Fuzzy Delphi ◽  
And Control ◽  
Individual Expert

Abstract In this research, the purpose was to design a fuzzy expert system based on fuzzy delphi method to detect and control the rice weed. The statistical population was elites and experts with regard to the science, experience and field of activity; 15 experts were selected as the sample. Two questionnaires were used to design the desired fuzzy expert: i) Fuzzy Delphi Technique Weed Detection Questionnaire, ii) Delphi Technique Weed Control Questionnaire. The design of the desired expert system was done with MATLAB software and the fuzzy logic tool box. That is, after obtaining an appropriate range of factors, through attributing the fuzzy trapezoidal membership functions to these ranges and generating the input functions, designing the rule base of this system and combining the output results of each factor, a system was designed whose input was the weed factor and the output was scores assigned to weeds. MATLAB guide was also used to design the graphical user interface. Then, for validation the designed system was tested. The answers of system and individual expert were then analyzed using paired t-test. Root Mean Square Error and Middle Absolute Value Deviation tests were used to calculate the system errors. The results were 0.12 and 0.01, respectively. This indicates that the designed fuzzy expert system has sufficient accuracy. Finally, given that all but two of the examined rules are the same as the diagnosis of an individual expert, then in 94% of the cases, the diagnosis of the system is the same as the diagnosis of an individual expert.

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