scholarly journals Sistem Inferensi Fuzzy: Pengertian, Penerapan, dan Manfaatnya

2021 ◽  
Vol 1 (2) ◽  
pp. 73-76
Author(s):  
Ummi Athiyah ◽  
Adela Putri Handayani ◽  
Muhammad Yusril Aldean ◽  
Novantri Prasetya Putra ◽  
Rafian Ramadhani
Keyword(s):  
Soft Computing ◽  
Non Linear

Logika fuzzy salah satu komponen pembentuk soft computing yang digunakan sebagai cara untuk memetakan masalah dari input ke output yang diharapkan. logika fuzzy memiliki beberapa kelebihan seperti mudah dimengerti karena memiliki konsep matematis yang sederhana, fleksibel untuk digunakan, terdapat toleransi pada data-data yang tidak tepat, mampu memodelkan fungsi-fungsi non-linear yang sangat kompleks, dapat menerapkan pengalaman pakar secara langsung tanpa proses pelatihan, dapat bekerja sama dengan teknik-teknik kendali secara konvensional, dan didasarkan pada bahasa alami. Logika fuzzy memiliki banyak peran di industri seperti bidang Kesehatan, Ilmu Ekonomi, Psikolog, dan Teknologi yang dapat membantu manusia dalam memecahkan suatu masalah dalam kehidupan. Dalam penerapan logika fuzzy terdapat beberapa proses, salah satunya yaitu sistem inferensi. Sistem inferensi merupakan kerangka komputasi yang didasarkan pada teori himpunan fuzzy, aturan fuzzy berbentuk IF-THEN, dan penalaran fuzzy. Manfaat dari inferensi fuzzy yaitu sebagai alat untuk mewakili pengetahuan yang berbeda tentang suatu masalah, serta untuk memodelkan interaksi. Dengan menggunakan metode penelitian studi literatur dari beberapa sumber, ditemukan banyak produk yang dikembangkan dari logika fuzzy seperti pengambilan keputusan, penentuan atau penilaian hasil, perangkat kendali jarak jauh, alat ukur, dan sistem pakar.  

scholarly journals Genetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth

2020 ◽  
Vol 1 (3) ◽  
pp. 13-27
Author(s):  
A. Stanley Raj ◽  
Y. Srinivas ◽  
R. Damodharan ◽  
B. Chendhoor ◽  
M. Sanjay Vimal
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Surface Characteristics ◽  
Advantages And Disadvantages ◽  
Resistivity Method ◽  
The Earth ◽  
Non Linear ◽  
Artificial Neural

Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.

scholarly journals Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

2021 ◽  
Vol 11 (18) ◽  
pp. 8290
Author(s):  
Muhammad Adnan Khan ◽  
Jürgen Stamm ◽  
Sajjad Haider
Keyword(s):  
Suspended Sediment ◽  
Soft Computing ◽  
Sediment Load ◽  
Local Linear Regression ◽  
Data Series ◽  
Suspended Sediment Load ◽  
Dynamic Features ◽  
Sediment Loads ◽  
Soft Computing Techniques ◽  
Non Linear

A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.

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