Titlebook: Computational Intelligence and Mathematics for Tackling Complex Problems; László T Kóczy,Jesús‘Medina-Moreno,Alexander Šosta Book 2020 Spr

秘密会议

NIP

Design of a Fuzzy System for Classification of Blood Pressure Load,hich is why it is important to analyze the blood pressure load, which indicates the daytime blood pressure load (% of diurnal readings ≥135/85 mmHg) and the nocturnal blood pressure load (% of nocturnal readings ≥120/70 mmHg). Different studies have shown the correlation between the blood pressure l

拾落穗

Triggering Probabilistic Neural Networks with Flower Pollination Algorithm,timization tasks. This paper is a example of utilizing this metaheuristic procedure for the Probabilistic Neural Network (PNN) learning process. In this paper, for the purpose of classification, this type of Neural Network is applied to data sets drawn from the UCI Machine Learning Repository. Moreo

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,Research on Improvement of Information Platform for Local Tourism by Paragraph Vector,ternet. These pieces of information include not only long sentences such as web pages and blogs, but also a lot of content of SNS composed of short sentences of about several words. Therefore, by the conventional search method, based on the occurrence probability of words in sentences, sufficient ac

Heresy

Recessive

Optimization Under Fuzzy Max-t-Norm Relation Constraints, applications they are used as constraints in optimization. Algorithms for specific objective functions have been proposed by many authors. In this paper we introduce a method to convert a system of fuzzy relation constraints with max-t-norm composition to a linear constraint system by adding intege

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https://doi.org/10.1007/978-3-642-38126-3r almost none, depending on the parameter selection. To solve this rapid changing in the number of the resulting lines, we introduced fuzzy Hough transform. If a fuzzified version of the weights of the individual points in the Hough transform is used, the inverse of the transform becomes clearer, re

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https://doi.org/10.1007/978-3-662-10076-9the other hand, we assert that the only way to construct group-like uninorms which have finitely many idempotents is to apply the last two extension methods consecutively, starting from a basic group-like uninorm. In this way a complete characterization for group-like uninorms which possess finitely