Model Reference Adaptive Fuzzy Control

Fuzzy control is a model-free linguistic control (if-then rules), which is easy to understand and provides nonlinear controllers for nonlinear systems. In recent years, some fuzzy controllers with an adaptive mechanism for unknown systems have been studied. In these studies, the parameters of a fuzzy controller are adjusted by some experience of human opereators or adaptive lows with some if-then rules. But the stability of the control system which is constructed by the plant and the fuzzy controller has not been analysed in most of these studies In this paper, we propose a class of Model Reference Adaptive Fuzzy Controllers for nonlinear systems. This class of controllers are the fuzzy controllers with the structure of the direct adaptive control system which can directly stabilize tracking error e. Finally, we derive the stability conditions (the adaptive laws) for the fuzzy controller for nonlinear systems by taking quadratic parameter error φ_i as the Lyapunov function V

ロバスト状態フィードバック制御則を構成するためのリアプノフの方法とそのロボット制御への応用

Robust stabilization for a class of nonlinear dynamical systems with uncertainties is investigated. Based on the stabilizability of a nominal system (i. e. the system in the absence of uncertainty), a class of continuous state feedback controllers for uncertain dynamical systems are presented. Compared with those reported in the control literature, such a class of stae feedback controllers are non-saturation type, have a rather simple form, and can guarantee practical stability and asymptotic stability of uncertain dynamical systems in terms of the choice of gain control function. Particularly, no chattering will appear in implementation for the control. A numerical example on the robust stabilization of a simple pendulum is given to demonstrate the utilization of the results. As an application of the results presented in this paper, the robust control problem of robot manipulators is also discussed, and a simpler robust control law for n-link robot manipulators is derived

On Lyapunov Theorem for Descriptor Systems

In this note, we present a different version of Lyapunov theorem for descriptor systems by introducing several new definitions

On Learning of Fuzzy Contoller with Neural Network

The fuzzy controller (FC) consists of two parts. First one is the control rule part which is referred to as linguistic rules. And it is written in the form of 'If∿THEN∿'. Second one is fuzzy reasoning which derives the reasoning value from the control rules. So it is easy for us to understand FC. However, there are two problems. First one is how to make the control rules. We usually take the rules from an expert. But it is not easy. Because all rules which the extpert has are not linguistic rules. The neural network has ability of learning. So many people expect that it is useful to get the control rules identified from the expert. And several researchers study with respect to it. Horikawa, one of them, presents a fuzzy controller using a neural network. The controller can automaticaly identify the control rules and tune the membership functions utilizing control data of the expert. The controller uses the rules written in the form of 'IF∿THEN y=f', where 'f' is constant. However, when FC uses this rules, it needs many rules. In this paper, we present a new fuzzy controller with a neural network which uses the rules written 'IF∿THEN y=f(・)', where f(・) is a linear function. Because when FC uses this rule, it need not many rules. In order to demonstrate the effectiveness of this FC, we simulate by using a computer to control a first order delayed system with a dead time. And we compare this controller with Horikawa's controller

Robust Decentralized Control of Large-Scale System with Delay in Interconnections and System Uncertainties Using Neural Networks

In this paper, we consider the decentralized robust stabilization problem of large-scale interconnected systems with delays, by making use of the theorem given by Lee and Radvic. If the mathematical system equation is complete, we can design decentlized control gains which satisfying the condition of the theorem so that the overall interconnected system is stable by using local state feedback. However, when the mathematical system equation includes model uncertainties, it is obvious that we can not make such interconncted systems stability by this controller. Thus, we propose the decentlized robust controller combining this controller into the multilayerd neural networks (MNN) to overcome system uncertainties

A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring and simulation of quantum many-body systems. In the last decade, research and development of QCs have rapidly advanced. Now hundreds of physical qubits are at our disposal, and one can find several remarkable experiments actually outperforming the classical computer in a specific computational task. On the other hand, it is unclear what the typical usages of the QCs are. Here we conduct an extensive survey on the papers that are posted in the quant-ph section in arXiv and claim to have used QCs in their abstracts. To understand the current situation of the research and development of the QCs, we evaluated the descriptive statistics about the papers, including the number of qubits employed, QPU vendors, application domains and so on. Our survey shows that the annual number of publications is increasing, and the typical number of qubits employed is about six to ten, growing along with the increase in the quantum volume (QV). Most of the preprints are devoted to applications such as quantum machine learning, condensed matter physics, and quantum chemistry, while quantum error correction and quantum noise mitigation use more qubits than the other topics. These imply that the increase in QV is fundamentally relevant, and more experiments for quantum error correction, and noise mitigation using shallow circuits with more qubits will take place.Comment: 14 pages, 5 figures, figures regenerate

Clinical implication of HLA class I expression in breast cancer

<p>Abstract</p> <p>Background</p> <p>Human leukocyte antigen (HLA)-class I molecules on tumor cells have been regarded as crucial sites where cytotoxic T lymphocytes (CTL) can recognize tumor-specific antigens and are strongly associated with anti-tumor activity. However, the clinical impact of HLA class I expression in breast cancer has not been clarified.</p> <p>Methods</p> <p>A total of 212 breast cancer patients who received curative surgery from 1993 to 2003 were enrolled in the current study. HLA class I expression was examined immunohistochemically using an anti-HLA class I monoclonal antibody. The correlation between HLA class I positivity and clinical factors was analyzed.</p> <p>Results</p> <p>The downregulation of HLA class I expression in breast cancer was observed in 69 patients (32.5%). HLA class I downregulation was significantly associated with nodal involvement (p < 0.05), TNM stage (p < 0.05), lymphatic invasion (p < 0.01), and venous invasion (p < 0.05). Patients with preserved HLA class I had significantly better disease-free interval (DFI) than those with loss of HLA class I (p < 0.05). However, in multivariable analysis, HLA class I was not selected as one of the independent prognostic factors of disease-free interval.</p> <p>Conclusion</p> <p>The examination of HLA class I expression is useful for the prediction of tumor progression and recurrent risk of breast cancer via the antitumor immune system.</p

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe