Greenhouse Fuzzy and Neuro-Fuzzy Modeling Techniques

Gorrostieta-Hurtado, Efren; Carlos, Pedraza-Ortega Jesus; Antonio, Aceves-Fernandez Marco; Manuel, Ramos-Arreguín Juan; Tovar-Arriaga, Saúl; Sotomayor-Olmedo, Artemio

doi:10.5772/35611

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Fuzzifier merupakan proses mengubah nilai variabel (nilai tajam) menjadi variable nilai fuzzy yang kompleks menggunakan fungsi keanggotaan [16]. Proses Defuzzifikasi merupakan proses kompleks untuk mengubah keluaran fuzzy yang diperoleh ke nilai sebenarnya [17][18] [19].…”

Section: Fuzzy Logicunclassified

A Fuzzy Rule-Based Fog Cloud Computing untuk Menganalisis Faktor Penyebab Kematian Ibu Meninggal Masa Postpartum

Septyanto¹

2021

JATISI

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Analisis penyebab kematian ibu pasca postpartum sangatlah penting dalam mengurang kematian ibu. Angka kematian ibu pasca postpartum diindonesia sangatlah tinggi sekitar 359 dalam 100.000 kelahiran. Faktor -faktor kematian ibu pasca postpartum jarang dianalisis lebih dalam sehingga tidak ada penanganan berkelanjuttan. Pada penelitian ini penulis menggunakan system fuzzy rule-based fog cloud computing untuk memonitoring kematian ibu berdasarkan tanda-tanda vital dan tanda-tanda fisik dengan menggunakan 10 parameter penyebab kematian ibu masa postpartum. Fog cloud digunakan untuk pengolahaan data dalam sever local agar mengurangi beban saat menggunakan cloud computing. Analisis awal parameter kematian ibu dirubah kedalam rentang low, medium dan high kemudian dimonitoring pada tujuh hari pasca postpartum dari hasil analisis yang dilakukan didapat faktor dominan kematian ibu pasca postpartum untuk tanda-tanda vital dipengaruhi oleh Blood Pressure dengan nilai MF 0,44 dengan status “High” untuk tanda-tanda fisik dipengaruhi oleh Anogenital Lochea Sanguinolenta dengan nilai MF 0,46 dengan status dalam system “High”. Parameter lain tidak terlalu berpengaruh terhadap kematian ibu cenderung turun dari hari pertama hingga hari ke tujuh pasca postpartum.

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Section: Fuzzy Logicunclassified

A Fuzzy Rule-Based Fog Cloud Computing untuk Menganalisis Faktor Penyebab Kematian Ibu Meninggal Masa Postpartum

Septyanto¹

2021

JATISI

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“…In the study [12], an Elman structure [13,14] is used as the basis for a recurrent neural network that is programmed to imitate greenhouse dynamics directly. The presentation [15] deals with the use of fuzzy cmeans clustering to build a fuzzy greenhouse climate model is compared against an artificial neural network (ANN) model and an adaptive neuro-fuzzy inference system (ANFIS) model. The authors use the adaptive neuro-fuzzy inference system to present a representation of the greenhouse design and control in the study [16].…”

Section: Introductionmentioning

confidence: 99%

Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique

Lachouri¹,

Mansouri²,

Lafifi³

2022

RIA

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The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and multiple outputs (responses). This project aimed to provide a solution, aided by artificial intelligence, to the issue of variations in time, input and output factors in a greenhouse internal climate that can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference and neural networks have been applied to mimic idealistic behavior. This paper proposes an adaptive system based on artificial neural networks technique embedded with fuzzy logic technique calls Adaptive Neuro Fuzzy Inference System (ANFIS) to predict air humidity, air temperature, internal radiation, and CO2 concentration while the seeds grow, in order to produce favorable greenhouse climate conditions. The input parameters include ten meteorological and control actuators that majorly influence tomato plants during their growth process in the greenhouse climate. This discussion revolves around a linguistic ANFIS model that will operate during the 48 days that it takes for the seedlings to grow. It will provide estimates of the greenhouse climate using meteorological data along with control actuators rooted in trained neural networks with back propagation optimization algorithm, and 500 iterations of the least square algorithm. Simulations have revealed the efficiency of this model.

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“…In [12], a recurrent neural network based on an Elman structure [13][14] is trained to emulate the direct dynamics of the greenhouse. In [15] the construction of fuzzy systems by fuzzy c-means for modeling www.ijacsa.thesai.org a greenhouse climate is described then the comparison with adaptive neuro-fuzzy inference system (ANFIS) and neural networks have presented. In [16] the authors have described the Greenhouse Design and Control using the adaptive neurofuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference Systems for Modeling Greenhouse Climate

Lachouri¹,

Mansouri²,

Lafifi³

et al. 2016

ijacsa

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Abstract-The objective of this work was to solve the problem of non linear time variant multi-input multi-output of greenhouse internal climate for tomato seedlings. Artificial intelligent approaches including neural networks and fuzzy inference have been used widely to model expert behavior. In this paper we proposed the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) as methodology to synthesize a robust greenhouse climate model for prediction of air temperature, air humidity, CO2 concentration and internal radiation during seedlings growth. A set of ten input meteorological and control actuators parameters that have a major impact on the greenhouse climate was chosen to represent the growing process of tomato plants. In this contribution we discussed the construction of an ANFIS system that seeks to provide a linguistic model for the estimation of greenhouse climate from the meteorological data and control actuators during 48 days of seedlings growth embedded in the trained neural network and optimized using the back propagation and the least square algorithm with 500 iterations. The simulation results have shown the efficiency of the proposed model.

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Greenhouse Fuzzy and Neuro-Fuzzy Modeling Techniques

Cited by 6 publications

References 12 publications

A Fuzzy Rule-Based Fog Cloud Computing untuk Menganalisis Faktor Penyebab Kematian Ibu Meninggal Masa Postpartum

A Fuzzy Rule-Based Fog Cloud Computing untuk Menganalisis Faktor Penyebab Kematian Ibu Meninggal Masa Postpartum

Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique

Adaptive Neuro-Fuzzy Inference Systems for Modeling Greenhouse Climate

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