scholarly journals Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles

2021 ◽  
Vol 2107 (1) ◽  
pp. 012048
Author(s):  
W M Nooriman ◽  
A H Abdullah ◽  
N Abdul Rahim ◽  
Erdy Sulino Mohd Muslim Tan
Keyword(s):  
Fuzzy Logic ◽  
Mangifera Indica ◽  
Optimal Growth ◽  
Growth Stages ◽  
Moderate Amount ◽  
Application Timing ◽  
Logic Control ◽  
K Fertilizer ◽  
The Right ◽  
Fertilizer Solution

Abstract Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia’s state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree’s demands is critical. The aim of this study is to create for Harumanis mango a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

MODELS FOR DESCRIBING AND SUPPORTING AUTOMATED SYSTEM SCENARIOS

2020 ◽  
pp. 18-22
Author(s):  
В.П. Хранилов ◽  
П.В. Мисевич ◽  
А.Э. Ермилов
Keyword(s):  
Fuzzy Logic ◽  
Life Cycle ◽  
Automated System ◽  
Just In Time ◽  
Success Factor ◽  
Situational Analysis ◽  
Frame Model ◽  
Information Logistics ◽  
The Right ◽  
The Mathematical Model

В статье представлены модели описания сценариев функционирования автоматизированных систем (АС). Вводится и анализируется категория "жизненный цикл сценариев АС". Наиболее важными этапами жизненного цикла сценария являются следующие: этап формирования событийного набора для формирования сценария, этап выполнения последовательности сценарных событий и этап ситуационного анализа внешней и внутренней среды события. В статье предложена математическая модель функционирования АС, которая используется для поддержки этапа выполнения последовательности сценарных событий и основана на принципе информационной логистики: каждый параметр (набор данных) находится в нужном месте в АС, "точно в срок" и в нужном формате. Для поддержки ситуационного анализа предлагается модифицированная фреймовая модель. Ситуационный анализ используется для разработки алгоритмов событий и определения следующего события в сценарии. Модифицированная фреймовая модель основана на использовании нечетких логических процедур в фреймовой сети. The paper presents models for describing the operating scenarios of automated systems. The authors introduce and analyze the category “the life cycle of automated system scenarios”. The life cycle consists of a sequence of stages. The leading success factor of any scenario is the support of the scenario during all stages of its life cycle. The most important stages of the scenario life cycle are the following: the stage of forming the event set for generating the scenario, the stage of performing the sequence of scenario events and the stage of situational analysis of the external and internal environment of the event. In the article it is proposed to use a theoretical set model in order to select an element from a set of alternatives. The elements are events for designing a scenario. The model uses fuzzy logic and is based on the process of controlling an array of parameters if variants are available. The model is used to support the stage of forming a set of events for generating the scenario. The mathematical model of automated system operation which is used to support the stage of performing the sequence of scenario events is suggested in the article. The model is based on the principle of information logistics: each parameter (a set of data) is in the right place in the automated system, ‘just-in-time’ and in the required format. A modified frame model is proposed to support situational analysis. The situational analysis is used to operate the event algorithms and to determine the next event in the scenario. The modified frame model is based on the use of fuzzy logic procedures in a frame network.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

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