Dynamics of Flapping Micro-Aerial Vehicles

[[abstract]]A dynamic-link rule base (DLRB) is introduced to the fuzzy inference systems for the purpose of speeding up and simplifying the fuzzy reasoning. This paper proposes a new reasoning mechanism by adding a dynamic-link rule base between the original rule base and the inference engine. The fuzzy inference system with a dynamic-link rule base is called a dynamic-link-rule-base-fuzzy-inference-system (DLRB-FIS). In the DLRB-FIS, only the fired rules, whose firing strengths are not equal to zero, are included for inference. The mathematical foundations, theorems and architecture of the DLRB-FIS are presented. A numeric example is also given for verifying the practicability of DLRB-FIS. The DLRB-FIS proposed has a general-purpose architecture. Therefore, it can be applied to many kinds of fields, such as fuzzy control, fuzzy image processing, fuzzy decision making, and fuzzy pattern recognition, etc[[conferencetype]]國際[[conferencedate]]20090610~20090612[[iscallforpapers]]Y[[conferencelocation]]St. Louis, US

Dynamic-link rule base in fuzzy inference system

[[abstract]]A dynamic-link rule base (DLRB) is introduced to the fuzzy inference systems for the purpose of speeding up and simplifying the fuzzy reasoning. This paper proposes a new reasoning mechanism by adding a dynamic-link rule base between the original rule base and the inference engine. The fuzzy inference system with a dynamic-link rule base is called a dynamic-link-rule-base-fuzzy-inference-system (DLRB-FIS). In the DLRB-FIS, only the fired rules, whose firing strengths are not equal to zero, are included for inference. The mathematical foundations, theorems and architecture of the DLRB-FIS are presented. A numeric example is also given for verifying the practicability of DLRB-FIS. The DLRB-FIS proposed has a general-purpose architecture. Therefore, it can be applied to many kinds of fields, such as fuzzy control, fuzzy image processing, fuzzy decision making, and fuzzy pattern recognition, etc.[[notice]]補正完畢[[conferencetype]]國際[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Short Term Load Forecasting New Year Celebration Holiday Using Interval Type-2 Fuzzy Inference System (Case Study: Java – Bali Electrical System)

Celebration of New Year In the Indonesian is constituted the one of the visit Indonesian’s tourism. This event course changes the load of electrical energy. The electrical energy providers that control and operation of electrical in Java and Bali (Java, Bali Electrical System) is required to be able to ensure continuity of load demand at this time, and forecast for the future. Short-term load forecasting very need to be supported by computational methods for simulation and validation. The one of computation’s methods is Interval Type – 2 Fuzzy Inference System (IT-2 FIS). Interval Type-2 Fuzzy Inference System (IT-2 FIS) as the development of methods of Interval Type-1 Fuzzy Inference System (IT-1 FIS), it is appropriate to be used in load forecasting because it has the advantages that very flexible on the change of the footprint of uncertainty (FOU), so it supports to establish an initial processing of the time series, computing, simulation and validation of system models. Forecasting methods used in this research are IT-2 FIS. The process for to know and analyzing the peak load a day is the specially day and 4 days before New year Celebration in the previous year continued analysis by using IT-2 FIS will be obtained at the peak load forecasting New Year Celebration in the coming year. This research shown the average of error value in 2012, 2013 and 2014 is 0,642%. This value is better than using the IT-1 FIS which has a value of error to 0.649%. This research concluded that IT-2 FIS can be used in Short Term Load Forecasting

A comparison of different fuzzy inference systems for prediction of catch per unit effort (CPUE) of fish

60-69Present work was aimed to design Mamdani- Fuzzy Inference System (FIS), Sugeno -FIS and Sugeno-Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the prediction of CPUE of fish. The system was implemented using MATLAB fuzzy toolbox. A prediction of CPUE was made using the models trained. The accuracy of fuzzy inference system models was compared using mean square error (MSE) and average error percentage. Comparative study of all the three systems provided that the results of Sugeno-ANFIS model (MSE =0.05 & Average error percentage=11.02%) are better than the two other Fuzzy Inference Systems. This ANFIS was tested with independent 28 dataset points. The results obtained were closer to training data (MSE=0.08 and Average error percentage=13.45%)

Fuzzy Conditional Inference and Application to Wireless Sensor Network

Zadeh, Mamdani, and TSK were proposed different fuzzy conditional inference for “if … then … “to approximate incomplete information. The Zadeh and Mamdani fuzzy conditional inferences require prior information for the consequent part. The TSK fuzzy conditional inference need not to know prior information for the consequent part, but it is difficult to compute. In this paper, new method is proposed for the position containing “if … then …” when prior information is not know the consequent part. Fuzzy Wireless Sensor Networks are discussed an application for proposed fuzzy conditional inference. Fuzzy inference system (FIS) is also discussed for WSN to detect Coastal erosion and Turbo Charger fuzzy controls System an examples

Penerapan Fuzzy Inference System Metode Mamdani Untuk Penentuan Besaran Persentase Beasiswa

Untuk menjamin pendidikan yang bermutu, pemerintah wajib memberikan layanan dan kemudahan terhadap pendidikan tanpa adanya deskriminasi. Untuk menyelenggarakan pendidikan yang bermutu diperlukan biaya yang cukup besar Oleh karena itu bagi setiap peserta didik pada setiap satuan pendidikan berhak mendapatkan biaya pendidikan bagi mereka yang orang tuanya tidak mampu membiayai pendidikannya, dan berhak mendapatkan beasiswa bagi mereka yang berprestasi. Penerapan Fuzzy Inference System (FIS) metode Mamdanidapat digunakan untuk pendukung keputusan dalam penentuan kelayakan beasiswa. Pada Fuzzy Inference System (FIS) metode Mamdani untuk memperoleh output diperlukan empat tahap, yaitu pembentukan himpunan fuzzy (Fuzzifikasi), pembentukan rules, aplikasi fungsi implikasi serta defuzzifikasi. Jenis penelitian ini merupakan jenis penelitian terapan, penelitian terapan ini merupakan suatu jembatan dari penelitian basic/murni diantara penelitian eksperimental. Teknik pengumpulan data yang dilakukan ada 3 metode yaitu Teknik Wawancara, Dokumentasi dan Observasi. Data yang didapatkan dari pihak marketing Fakultas Ilmu Komputer-Universitas Bandar Lampung. Tujuan dari penelitian ini adalah penerapan algoritma dan Fuzzy Inference System dengan Metode MAMDANI untuk penentuan kelayakan beasiswa mahasiswa baru, sehingga beasiswa diterima oleh calon mahasiswa tersebut tepat dan objektif. Hasil dari penelitian ini merupakan penerapan fuzzy inference system mamdani dilakukan dengan menggunakan 4 tahap yaitu pembentukan himpunan fuzzy, pembuatan aturan fuzzy(inference), fungsi implikasi dari setiap aturan dan Tahap terakhir adalah defuzifikasi. Dan Hasil lain dari penelitian ini bahwa fuzzy inference system mamdani dapat digunakan untuk penentuan kelayakan beasiswa sehingga beasiswa yang diberikan tersebut tepat dan objektif

Fuzzy Logic in neurosurgery: Predicting poor outcomes after lumbar disk surgery in 501 consecutive patients

Background: Despite a lot of research into Patient selection, a significant number of Patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying Patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.Methods: Data of 501 Patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative Patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.Results: Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of Patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.Conclusion: Fuzzy inference system is a sensitive method of predicting Patients who will fail to improve with surgical intervention

Detecting Student Dropouts Using Fuzzy Inferencing

Fuzzy logic provides a methodology for reasoning using imprecise rules and assertions. Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. This study concerns the development of a Fuzzy Inference System (FIS) for identifying likely student dropouts at Columbus State University (CSU). The fuzzy inference based model uses a hybrid knowledge extraction process to predict how likely each freshman student will be to drop their program of study at the end of their first semester. This process uses both a top down (symbolic) and a bottom-up (data-based) approach. Historical student records data have been used to evaluate the developed FIS. Findings of this study indicate that the FIS does not perform better than an Artificial Neural Network (ANN) developed for the same purpose, but useful insights about how different student attributes relate to their retention or departure may be gained from the rules that define the fuzzy model

PENERAPAN METODE FIS MAMDANI UNTUK KLASIFIKASI KEMATANGAN MELINJO BERDASARKAN WARNA

ABSTRAKLogika fuzzy merupakan metode penalaran yang digunakan sebagai penyelesaian permasalahan yang mengandung keraguan (kabur). Fuzzy inference system (FIS) merupakan metode dengan aturan logika fuzzy yang mulai dikembangkan dalam berbagai bidang seperti sistem kontrol, klasifikasi dan berbagai sistem lainnya. Penelitian ini menerapkan FIS sebagai penentuan tingkat kematangan melinjo (Gnetum gnemon L.) dengan mengklasifikasi warna biji melinjo. Penelitian ini bertujuan untuk menerapkan FIS dalam pengklasifikasian kematangan warna biji melinjo dengan pendekatan warna RGB. Penerapan ini melihat kesalahan klasifikasi dan rentang tingkat kematangan biji melinjo. Pembentukan sistem FIS ini menggunakan metode Mamdani dengan data gambar biji melinjo yang diproses menggunakan warna primer adiktif atau Red, Green, Blue (RGB). Variabel yang digunakan sebanyak 4 buah, terdiri dari 3 variabel input dan 1 variabel output. Variabel input berupa variabel Red, variabel Green dan variabel Blue dengan masing-masing variabel input memiliki 3 parameter yaitu parameter Rendah, Normal dan Tinggi. Variabel output merupakan persentase kandungan warna merah (Red) dengan 3 tingkat kematangan yaitu Mentah, Mengkal, Masak. Hasil penelitian ini menunjukkan bahwa kesalahan klasifikasi hanya terjadi pada tingkat kematangan Mengkal dengan persentase kesalahan sebesar 5%. Kata kunci: fuzzy inference system, mamdani, melinjo, RG