Fuzzy identification of systems and its applications to modeling and control

Takagi, Toshihisa; Sugeno, Michio

doi:10.1109/tsmc.1985.6313399

Cited by 16,971 publications

(6,132 citation statements)

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“…Fuzzy logic controller is one of the controllers based on "if-then" rules that can be used for decision making as in the human mind [16]. At this research, Takagi-Sugeno's fuzzy inference system (TS FIS) [17] with the input engine torque error and derivative error and output weighting to absolute gain control is used to determine the correction to the throttle plate angle given by the driver. Figure 4 show membership functions of input and output fuzzy logic controller (TS FIS) to be used, since we need fuzzy inference system as a soft-shift scheduling of control gain appropriate to different engine operations.…”

Section: Methodsmentioning

confidence: 99%

Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control

Triwiyatno¹,

Sumardi²

2012

MST

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In the case of injection gasoline engine, or better known as spark ignition engines, an effort to improve engine performance as well as to reduce fuel consumption is a fairly complex problem. Generally, engine performance improvement efforts will lead to increase in fuel consumption. However, this problem can be solved by implementing engine torque control based on intelligent regulation such as the fuzzy logic inference system. In this study, fuzzy logic engine torque regulation is used to control the throttle position entered by the driver to achieve optimal engine torque. An engine torque vs. throttle position and engine speed mapping for vehicles with economical function is used to build this control process regulation. From the simulation result, it can be concluded that this control strategy is very effective to reduce fuel consumption and simultaneously to optimize the engine performance. Abstrak Strategi Penghematan Bahan Bakar pada Mesin dengan Pemicu Busi Melalui Kontrol Torsi Mesin denganLogika Fuzzy. Pada kasus kontrol mesin bensin, atau lebih dikenal sebagai mesin pengapian busi, upaya untuk meningkatkan kinerja mesin sekaligus mengurangi konsumsi bahan bakar adalah masalah yang cukup kompleks. Umumnya, upaya peningkatan performa mesin akan menyebabkan peningkatan konsumsi bahan bakar. Namun, hal ini dapat diselesaikan dengan melakukan kontrol torsi mesin berdasarkan peraturan cerdas seperti sistem inferensi logika fuzzy. Dalam studi ini, regulasi torsi mesin dengan logika fuzzy digunakan untuk mengontrol posisi throttle yang dimasukkan oleh pengemudi untuk mencapai torsi mesin yang optimal. Pemetaan torsi mesin vs posisi throttle dan kecepatan putar mesin untuk kendaraan dengan tujuan ekonomis digunakan untuk mendesain aturan proses kontrol. Dari hasil simulasi dapat disimpulkan bahwa strategi kontrol dengan logika fuzzy sangat efektif untuk mengurangi konsumsi bahan bakar dan sekaligus mengoptimalkan kinerja mesin.

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Section: Methodsmentioning

confidence: 99%

Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control

Triwiyatno¹,

Sumardi²

2012

MST

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“…The Takagi and Sugeno (TS) (Takagi & Sugeno 1985) fuzzy model is applied. Figure 3 illustrates the ANFIS model structure and MF used in this paper.…”

Section: Anfis Modelmentioning

confidence: 99%

Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Kaloop

El-Diasty

2017

Geomatics, Natural Hazards and Risk

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Accurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the leastsquares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average.

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“…IF-THEN rules are commonly specified by a field expert, although they can also be learned from the existing data (Jang, 1993). Two most important types of the FIS are Mamdani (Mamdani, 1977) and Takagi-Sugeno (Takagi &Sugeno, 1985). In the Mamdani system, both inputs and outputs are presented as fuzzy sets, so these systems are very easy to interpret.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%