Fuzzy Inference Engines

2003 ◽  
pp. 161-172 ◽  
Keyword(s):  
Fuzzy Inference ◽  
Inference Engines

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2001 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2003 ◽  
Vol 10 (2) ◽  
pp. 81-95 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines ◽  
First Case

Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.In this work a fuzzy logic controller is introduced with the idea of “split-horizon”; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent sub-controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FIE). Computer simulations are used to verify the performance of the controller. Three simulation cases are presented. In the first case, the crane is operated in the gantry (radial) mode in which the trolley moves along the jib while the jib is fixed. In the second case (rotary mode), the trolley moves along the jib and the jib rotates. In the third case, the trolley and jib are fixed while the load is given an initial disturbance. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing the maneuvers in relatively reasonable times.

Advanced fuzzy inference engines in situation aware computing

2010 ◽  
Vol 161 (4) ◽  
pp. 498-521 ◽  
Author(s):  
Christos Anagnostopoulos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Fuzzy Inference ◽  
Inference Engines

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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