Neuro Fuzzy Studies of Effect of Flexibilities on Performance of Flexible Manufacturing System

2012 ◽  
Vol 622-623 ◽  
pp. 56-59
Author(s):  
Durgesh Sharma ◽  
Suresh Kumar Garg ◽  
Chitra Sharma
Keyword(s):  
Fuzzy Inference System ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Fuzzy Inference ◽  
Control Strategies ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Control ◽  
Different Levels

The paper presents a Neuro-fuzzy study of Flexible Manufacturing System subject to different design and control strategies. Adaptive Neuro-Fuzzy inference system (ANFIS) techniques have been used to evaluate the performance. The objective of our work is to evaluating the performances of system in terms of Make Span Time at different levels of Routing and Machine flexibilities.

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 Neuro-fuzzy control of sit-to-stand motion using head position tracking

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1342-1353
Author(s):  
Samina Rafique ◽  
M Najam-ul-Islam ◽  
M Shafique ◽  
A Mahmood
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Head Position ◽  
Task Space ◽  
Body Segment ◽  
Inference System ◽  
Sit To Stand ◽  
Neuro Fuzzy ◽  
Control Scheme ◽  
And Control

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

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