Knowledge Discovery by Extracting Interpretable Fuzzy Rules

2003 ◽  
pp. 173-204
Author(s):  
Yaochu Jin
Keyword(s):  
Knowledge Discovery ◽  
Fuzzy Rules

Use of Compensatory Fuzzy Logic for Knowledge Discovery Applied to the Warehouse Order Picking Problem for Real-Time Order Batching

2019 ◽  
pp. 62-88
Author(s):  
Laura Cruz-Reyes ◽  
Rafael Alejandro Espin-Andrade ◽  
Fernando López Irrarragorri ◽  
César Medina-Trejo ◽  
José Fernando Padrón Tristán ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Discovery ◽  
Real Time ◽  
Classical Logic ◽  
Order Picking ◽  
Fuzzy Rules ◽  
Order Batching ◽  
Time Order ◽  
A Company

Order picking is an activity in a warehouse, carried out by a team, where customer orders are prepared. It is also the activity with the highest logistic costs in a company. In this chapter, a proposal is presented aimed at improving the order picking process in a warehouse. Order picking continues to attract the attention of people in charge of optimizing processes in a company for minimizing costs. There are several approaches to order picking such as discrete picking, batch picking, and zone picking. The purpose of this study is mainly oriented toward the reduction of batch picking times to achieve an improvement in the service. In this chapter is proposed the application of compensatory fuzzy logic (CFL), which is a branch of classical logic and is also discussed the possibility of making real-time order batches (dynamic batching) by using compensatory fuzzy rules for the classification of incoming orders.

Development of Knowledge Discovery System from Annotated Time Sereis Data

2012 ◽  
Vol 132 (4) ◽  
pp. 592-597
Author(s):  
Hiroshi Sugimura ◽  
Kazunori Matsumoto
Keyword(s):  
Knowledge Discovery ◽  
Discovery System

Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

EVALUATION OF FUZZY RULES EXTRACTED FROM DATA

2001 ◽  
Vol 06 (02) ◽  
Author(s):  
Adam Schenker ◽  
Abraham Kandel ◽  
Mark Last
Keyword(s):  
Fuzzy Rules

Tuning of PI Controller for A Conical Tank Process with Fuzzy Rules

2014 ◽  
Vol 2 (3) ◽  
pp. 6-10
Author(s):  
Gireesh Namineni ◽  
◽  
G. Sreenivasulu ◽  
Keyword(s):  
Pi Controller ◽  
Fuzzy Rules

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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