Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. As opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise, SVM is considered an effective method of data classification. One of the techniques used to overcome this inefficiency is fuzzy logic, with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies. The result further showed that using FSVM with a Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PD

WIYN Open Cluster Study. XXXVIII. Stellar Radial Velocities in the Young Open Cluster M35 (NGC 2168)

We present 5201 radial-velocity measurements of 1144 stars, as part of an ongoing study of the young (150 Myr) open cluster M35 (NGC 2168). We have observed M35 since 1997, using the Hydra Multi-Object Spectrograph on the WIYN 3.5m telescope. Our stellar sample covers main-sequence stars over a magnitude range of 13.0<V<16.5 (1.6 - 0.8 Msun) and extends spatially to a radius of 30 arcminutes (7 pc in projection at a distance of 805 pc or 4 core radii). Due to its youth, M35 provides a sample of late-type stars with a range of rotation periods. Therefore, we analyze the radial-velocity measurement precision as a function of the projected rotational velocity. For narrow-lined stars (v sin i < 10 km/s), the radial velocities have a precision of 0.5 km/s, which degrades to 1.0 km/s for stars with v sin i = 50 km/s. The radial-velocity distribution shows a well-defined cluster peak with a central velocity of -8.16 +/- 0.05 km/s, permitting a clean separation of the cluster and field stars. For stars with >=3 measurements, we derive radial-velocity membership probabilities and identify radial-velocity variables, finding 360 cluster members, 55 of which show significant radial- velocity variability. Using these cluster members, we construct a color-magnitude diagram for our stellar sample cleaned of field star contamination. We also compare the spatial distribution of the single and binary cluster members, finding no evidence for mass segregation in our stellar sample. Accounting for measurement precision, we place an upper limit on the radial-velocity dispersion of the cluster of 0.81 +/- 0.08 km/s. After correcting for undetected binaries, we derive a true radial-velocity dispersion of 0.65 +/- 0.10 km/s.Comment: accepted for publication in A

Studi Tentang Diagram Kontrol T2 Hotelling Fuzzy Dan W2 Serta Aplikasinya Pada Proses Produksi Tepung Terigu Palapa Di PT. Pioneer Flour Mill Industries

Suatu produk memiliki critical quality tertentu. Produk dikategorikan cacat apabila berada di luar dari batas spesifikasi yang telah ditentukan. Pengukuran karakteristik kualitas berjenis variabel memungkinkan terjadi kesalahan pengukuran. Salah satu contoh kesalahan pengukuran adalah nilai pengukuran tidak sesuai dengan nilai aktual. Nilai pengukuran seringkali berada di sekitar nilai aktual, selisih tersebut yang dapat menyebabkan ambiguitas. Jika hasil pengukuran suatu produk menghasilkan suatu bilangan tertentu, maka terdapat kemungkinan hasil pengukuran sebenarnya berada di kisaran bilangan tertentu sebelumnya. Selisih tersebut yang dapat menyebabkan ambiguitas. Ambiguitas pada produk karakteristik kualitas variabel dapat diatasi dengan menggunakan diagram kontrol T2 Hotelling Fuzzy yang merupakan pengembangan dari Diagram Kontrol T2 Hotelling. Pada penelitian ini akan dibangun diagram kontrol T2 Hotelling Fuzzy dengan pendekatan fungsi keanggotaan dan diagram kontrol W2 dengan pendekatan probabilitas. PT. Pioneer Flour Industries telah melakukan pengontrolan terhadap proses produksi tepung terigu “Palapa”. Variabel karakteristik kualitas yang dikontrol adalah moisture (X1), glutten (X2), dan ash (X3). Fungsi keanggotaan untuk X1, X2, dan X3 adalah fungsi keanggotaan gabungan antara kurva segitiga, trapesium, dan linear. Dari hasil analisis dan pembahasan didapatkan kesimpulan bahwa lebih baik menggunakan diagram kontrol W2 dengan pendekatan probabilitas dibandingkan diagram kontrol T2 Hotelling Fuzzy dengan pendekatan fungsi keanggotaan. Dengan menerapkan diagram kontrol T2 Hotelling Fuzzy dengan pendekatan fungsi keanggotaan dengan 4 skenario menunjukkan bahwa proses terkendali, tetapi dengan menerapkan diagram kontrol W2 dengan pendekatan probabilitas menunjukkan bahwa proses produksi tidak terkendali. ===================================================================================================== A product has a certain critical quality. A product is categorized as a defective product when that product is out of defined specification limit. The measurement of characteristic quality typed variable has a possibility to have an error measurement. One of the error samples is a measurement value that did not match to the actual value. The measurement value is often placed around its actual value, the difference can produce ambiguity. If the measurement results of a certain product produce a specific number, then it has a possibility that the actual measurement is around the previous specific number. That difference can produce ambiguity. The ambiguity of the product of characteristic quality variable can be handled with T2 Hotelling Fuzzy control chart which is the development from T2 Hotelling control chart. In this research were built the T2 Hotelling Fuzzy control chart with membership function approach, and W2 control chart with probability approach. PT. Pioneer Flour Industries has controlled the productivity process of the “Palapa” white flour. The controlled characteristic quality variables were moisture (X1), glutten (X2), and ash(X3). The membership function for X1, X2, X3 is the combined membership function between triangle, trapezium, and linear curve. From the analysis and discussion obtained the conclusion that is better to use W2 control chart using probability approach than T2 Hotelling Fuzzy control chart using membership function approach. By applying T2 Hotelling Fuzzy control chart using membership function approach with four scenarios shows that the process is in control, but by applying W2 control chart using probability approach shows that the process is out of control

Probing quantum state space: does one have to learn everything to learn something?

Determining the state of a quantum system is a consuming procedure. For this reason, whenever one is interested only in some particular property of a state, it would be desirable to design a measurement setup that reveals this property with as little effort as possible. Here we investigate whether, in order to successfully complete a given task of this kind, one needs an informationally complete measurement, or if something less demanding would suffice. The first alternative means that in order to complete the task, one needs a measurement which fully determines the state. We formulate the task as a membership problem related to a partitioning of the quantum state space and, in doing so, connect it to the geometry of the state space. For a general membership problem we prove various sufficient criteria that force informational completeness, and we explicitly treat several physically relevant examples. For the specific cases that do not require informational completeness, we also determine bounds on the minimal number of measurement outcomes needed to ensure success in the task.Comment: 23 pages, 4 figure

Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p

Learning Membership Functions in a Function-Based Object Recognition System

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.Comment: See http://www.jair.org/ for any accompanying file

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses