Generalization of Fuzzy Inference System Based on Boolean and Kleenean Relations FIS-BKR for Modelling and Control

2017 ◽  
pp. 351-364
Author(s):  
Erika Zutta ◽  
Jhonattan Gantiva ◽  
Jairo Soriano
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
And Control

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 Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy

2021 ◽  
Vol 8 (1) ◽  
pp. 157
Author(s):  
Fachrul Rozie ◽  
Iwan Syarif ◽  
Muhammad Udin Harun Al Rasyid ◽  
Edi Satriyanto
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Smart Phone ◽  
Fish Farming ◽  
Water Circulation ◽  
Inference System ◽  
And Control ◽  
Internet Of Things Technology

<p class="Abstrak">Akuaponik adalah penggabungan sistem budidaya akuakultur dan hidroponik yang dapat menjadi solusi untuk mengatasi keterbatasan lahan, keterbatasan sumber air serta meningkatkan ketahanan pangan. Pada sistem akuaponik, kualitas air pada budidaya ikan merupakan salah satu syarat utama dalam keberhasilan proses budidaya. Penelitian ini mengkombinasikan peternakan lele dengan penanaman kangkung hidroponik. Kotoran ikan lele dan sisa makanan terakumulasi di air dan dapat menjadi racun bagi ikan lele karena mengandung kadar anomia yang tinggi sehingga sangat berbahaya jika tidak dibuang. Air ini kemudian dialirkan ke tanaman kangkung hidroponik melalui biofilter yang bermanfaat sebagai pengurai air kotor dari kolam menjadi nitrat dan nitrit yang berguna sebagai nutrisi tanaman. Selanjutnya setelah air menjadi bersih dan mempunyai kadar oksigen yang tinggi, air tersebut dialirkan kembali ke kolam ikan lele. Penelitian ini bertujuan untuk mengembangkan sistem cerdas pada budidaya akuaponik dengan memanfaatkan teknologi <em>Internet of Things</em><em> </em>yang dilengkapi dengan beberapa jenis sensor untuk memantau dan mengendalikan kualitas air dengan menerapkan algoritma Sistem Inferensi Fuzzy /<strong><em> </em></strong><em>Fuzzy Inference System </em>(FIS) untuk mengatur kecepatan sirkulasi air kolam agar menghemat daya listrik pada pompa<em>.</em> Peralatan ini juga dilengkapi dengan layanan pemberian pakan ikan secara otomatis yang dapat diprogram sesuai kebutuhan. Sistem akuaponik ini dapat dipantau melalui web maupun ponsel pintar berbasis android. Pengujian yang dilakukan terhadap perbandingan keputusan oleh pakar dan sistem FIS pada kecepatan sirkulasi air sistem akuaponik menunjukkan nilai akurasi 83,33%, dan hasil dari pengujian ketepatan alat pemberi pakan yang dibuat secara otomatis terhadap ketepatan pemberian pakan secara manual memiliki nilai akurasi 90,97%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul21"><em><span lang="IN">Aquaponics is a combination of aquaculture and hydroponic cultivation systems that can be a solution to overcoming limited land, limited water sources and increasing food security. In the aquaponics system, water quality in fish farming is one of the main requirements in the success of the cultivation process. This research combines catfish farming with hydroponic kale cultivation. Catfish feces and food scraps accumulate in water and can be toxic to catfish because they contain high levels of anomia so it is very dangerous if not disposed of. This water is then flowed to hydroponic kale plants through a biofilter which is useful as decomposing dirty water from the pond into nitrates and nitrites which are useful as plant nutrients. Furthermore, after the water becomes clean and has high oxygen levels, the water is flowed back into the catfish pond. This study aims to develop a smart system in aquaponic cultivation by utilizing Internet of Things technology which is equipped with several types of sensors to monitor and control water quality by applying the Fuzzy Inference System (FIS) algorithm to regulate the speed of pool water circulation in order to save electric power on the pump. This equipment is also equipped with an automatic fish feeding service which can be programmed as needed. This aquaponics system can be monitored via the web or an Android-based smart phone. Tests carried out on the comparison of decisions by experts and the FIS system on the water circulation speed of the aquaponics system show an accuracy value of 83.33%, and the results of testing the accuracy of the feeder that is made automatically against the accuracy of manual feeding have an accuracy value of 90.97% .</span></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI

2021 ◽  
Vol 11 (2) ◽  
pp. 73-80
Author(s):  
Maria Yunita Nesi ◽  
Yampi R Kaesmetan ◽  
Meliana O. Meo
Keyword(s):  
Expert System ◽  
Fuzzy Inference System ◽  
Prevention And Control ◽  
Fuzzy Inference ◽  
The Body ◽  
Parasitic Infections ◽  
Inference System ◽  
The Right ◽  
And Control ◽  
Mamdani Fuzzy Inference System

The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.

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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