نتایج جستجو برای: variant membership function

تعداد نتایج: 1316401  

2014
Sheng Miao Robert J. Hammell Timothy Hanratty Ziying Tang

Network-centric military operations are redefining information overload as military commanders and staffs are inundated with vast amounts of information. Recent research has developed a fuzzy-based system to assign a Value of Information (VoI) determination for individual pieces of information. This paper presents an investigation on the effect of using triangular and trapezoidal fuzzy membersh...

Journal: :Expert Syst. Appl. 2009
S. K. Bhattacharya S. K. Goswami

A fuzzy based method has been proposed for identification of probable capacitor nodes of radial distribution system. Simulated annealing technique has been used for final selection of the capacitor sizes. New fuzzy membership functions have been formulated where the active power membership is an exponential function of the nodal per unit active power and branch active power loss, the reactive m...

2008
Aida Vitória Andrzej Szalas Jan Maluszynski

Rough set approximations of Pawlak [15] are sometimes generalized by using similarities between objects rather than elementary sets. In practical applications, both knowledge about properties of objects and knowledge of similarity between objects can be incomplete and inconsistent. The aim of this paper is to define set approximations when all sets, and their approximations, as well as similari...

Journal: :JNW 2014
Yu Liu You He Kai Dong Haipeng Wang

A fuzzy binary track correlation algorithm and a fuzzy classical assignment algorithm are proposed for distributed multisensor data fusion in this paper. First, the corresponding composition of fuzzy element sets and selection of membership function are discussed. And then, dynamic assignment of weight vectors and multi-valency processing methods are designed. Finally, a fuzzy binary track corr...

2011
Carlos Lopez-Molina Humberto Bustince Javier Fernández Bernard De Baets

The fuzzy representation of the edges has been widely studied in different works. Generally, for each pixel, the authors use membership degrees linearly proportional to the magnitude of the gradient at that position of the image. This would be equivalent to using a triangular membership functions on the gradient magnitude. In this work we study the use of parametric functions in the transformat...

1996
Harry Buhrman Leen Torenvliet

We show that any p-selective and self-reducible set is in P. As the converse is also true, we obtain a new characterization of the class P. A generalization and several consequences of this theorem are discussed. Among other consequences, we show that under reasonable assumptions auto-reducibility and self-reducibility diier on NP, and that there are non-p-T-mitotic sets in NP.

Using the idea behind the Tillich-Zémor hash function, we propose a new hash function. Our hash function is parallelizable and its collision resistance is implied by a hardness assumption on a mathematical problem. Also, it is secure against the known attacks. It is the most secure variant of the Tillich-Zémor hash function until now.

2009
Manish Sarkar

This paper generalizes the concepts of rough membership functions in pattern classification tasks to fuzz rough membership functions. Unlike the rough membersgp value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the fuzzy-rough membership value of the pattern signlfies the rou h uncertainty as well as the . fuzz uncertainty associated wig it. ...

2007
Sergio Donoso Nicolás Marín M. Amparo Vila

Fuzzy regression models has been traditionally considered as a problem of linear programming. The use of quadratic programming allows to overcome the limitations of linear programming as well as to obtain highly adaptable regression approaches. However, we verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this prob...

2009
Pragnesh A. Gajjar Jonathan M. Garibaldi

In this paper we study some properties related to the distribution of membership grades for non-stationary fuzzy sets. We obtain the formulation for the distribution, where the non-stationary fuzzy sets are obtained by generating instantiations about the center values. The two cases considered are for the underlying membership functions as Triangular and Gaussian. The analytical results obtaine...

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function paginate(evt) { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term pg=parseInt(evt.target.text) var data={ "year":filter_year, "term":term, "pgn":pg } filtered_res=post_and_fetch(data,url) window.scrollTo(0,0); } function update_search_meta(search_meta) { meta_place=document.getElementById("search_meta_data") term=search_meta.term active_pgn=search_meta.pgn num_res=search_meta.num_res num_pages=search_meta.num_pages year=search_meta.year meta_place.dataset.term=term meta_place.dataset.page=active_pgn meta_place.dataset.num_res=num_res meta_place.dataset.num_pages=num_pages meta_place.dataset.year=year document.getElementById("num_result_place").innerHTML=num_res if (year !== "unfilter"){ document.getElementById("year_filter_label").style="display:inline;" document.getElementById("year_filter_place").innerHTML=year }else { document.getElementById("year_filter_label").style="display:none;" document.getElementById("year_filter_place").innerHTML="" } } function update_pagination() { search_meta_place=document.getElementById('search_meta_data') num_pages=search_meta_place.dataset.num_pages; active_pgn=parseInt(search_meta_place.dataset.page); document.getElementById("pgn-ul").innerHTML=""; pgn_html=""; for (i = 1; i <= num_pages; i++){ if (i===active_pgn){ actv="active" }else {actv=""} pgn_li="
  • " +i+ "
  • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->