Designing Sounds and Spaces: Interdisciplinary Rules & Proportions in Generative Stochastic Music and Architecture
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A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation
fractal and fractional Article A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation Marylu L. Lagunes, Oscar Castillo * , Fevrier Valdez , Jose Soria and Patricia Melin Tijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Tijuana 22379, Mexico; [email protected] (M.L.L.); [email protected] (F.V.); [email protected] (J.S.); [email protected] (P.M.) * Correspondence: [email protected] Abstract: Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion. Keywords: fractal search; fuzzy logic; parameter adaptation; CEC 2017 Citation: Lagunes, M.L.; Castillo, O.; Valdez, F.; Soria, J.; Melin, P. A New Approach for Dynamic Stochastic 1. Introduction Fractal Search with Fuzzy Logic for Metaheuristic algorithms are applied to optimization problems due to their charac- Parameter Adaptation. Fractal Fract. teristics that help in searching for the global optimum, while simple heuristics are mostly 2021, 5, 33. -
Introduction to Stochastic Processes - Lecture Notes (With 33 Illustrations)
Introduction to Stochastic Processes - Lecture Notes (with 33 illustrations) Gordan Žitković Department of Mathematics The University of Texas at Austin Contents 1 Probability review 4 1.1 Random variables . 4 1.2 Countable sets . 5 1.3 Discrete random variables . 5 1.4 Expectation . 7 1.5 Events and probability . 8 1.6 Dependence and independence . 9 1.7 Conditional probability . 10 1.8 Examples . 12 2 Mathematica in 15 min 15 2.1 Basic Syntax . 15 2.2 Numerical Approximation . 16 2.3 Expression Manipulation . 16 2.4 Lists and Functions . 17 2.5 Linear Algebra . 19 2.6 Predefined Constants . 20 2.7 Calculus . 20 2.8 Solving Equations . 22 2.9 Graphics . 22 2.10 Probability Distributions and Simulation . 23 2.11 Help Commands . 24 2.12 Common Mistakes . 25 3 Stochastic Processes 26 3.1 The canonical probability space . 27 3.2 Constructing the Random Walk . 28 3.3 Simulation . 29 3.3.1 Random number generation . 29 3.3.2 Simulation of Random Variables . 30 3.4 Monte Carlo Integration . 33 4 The Simple Random Walk 35 4.1 Construction . 35 4.2 The maximum . 36 1 CONTENTS 5 Generating functions 40 5.1 Definition and first properties . 40 5.2 Convolution and moments . 42 5.3 Random sums and Wald’s identity . 44 6 Random walks - advanced methods 48 6.1 Stopping times . 48 6.2 Wald’s identity II . 50 6.3 The distribution of the first hitting time T1 .......................... 52 6.3.1 A recursive formula . 52 6.3.2 Generating-function approach . -
1 Stochastic Processes and Their Classification
1 1 STOCHASTIC PROCESSES AND THEIR CLASSIFICATION 1.1 DEFINITION AND EXAMPLES Definition 1. Stochastic process or random process is a collection of random variables ordered by an index set. ☛ Example 1. Random variables X0;X1;X2;::: form a stochastic process ordered by the discrete index set f0; 1; 2;::: g: Notation: fXn : n = 0; 1; 2;::: g: ☛ Example 2. Stochastic process fYt : t ¸ 0g: with continuous index set ft : t ¸ 0g: The indices n and t are often referred to as "time", so that Xn is a descrete-time process and Yt is a continuous-time process. Convention: the index set of a stochastic process is always infinite. The range (possible values) of the random variables in a stochastic process is called the state space of the process. We consider both discrete-state and continuous-state processes. Further examples: ☛ Example 3. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f0; 1; 2; 3; 4g representing which of four types of transactions a person submits to an on-line data- base service, and time n corresponds to the number of transactions submitted. ☛ Example 4. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f1; 2g re- presenting whether an electronic component is acceptable or defective, and time n corresponds to the number of components produced. ☛ Example 5. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2;::: g representing the number of accidents that have occurred at an intersection, and time t corresponds to weeks. ☛ Example 6. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2; : : : ; sg representing the number of copies of a software product in inventory, and time t corresponds to days. -
Away with Apps? Interactive Web 3D Driven by Relational Databases Jelle Vermandere
Away with apps? Interactive web 3D driven by relational databases Jelle Vermandere Away with apps? Interactive web 3D driven by relational databases. Jelle Vermandere Student number: 01306207 Supervisors: Willem Bekers, Prof. ir.-arch. Ruben Verstraeten Counsellor: Nino Heirbaut Master's dissertation submitted in order to obtain the academic degree of Master of Science in de ingenieurswetenschappen: architectuur Academic year 2019-2020 The author gives permission to make this master dissertation available for consultation and to copy parts of this master dissertation for personal use. In all cases of other use, the copyright terms have to be respected, in particular with regard to the obligation to state explicitly the source when quoting results from this master dissertation. 13/01/2020 Foreword This thesis aims to make the process of adding 3D models to relational databases easier while providing more functionality. Before I explain this, I would like to thank everyone who made my life easier. First of all, my supervisors Willem Bekers and Ruben Verstraeten for their valuable guidance sessions, their inspiring ideas and their support in my journey to the completion of this thesis. I would also express my gratitude towards my favourite spelling checkers Lotte and Tessa for their extreme focus on letters and punctuation marks and Jonathan for the more rigorous language corrections. Special thanks goes to my sister Floor for being my lay-out support and showing me the wonderful world of web development. Thanks to the numerous people who created forum posts and online tutorials who helped me solve many problems. I have learned many new skills along the way, many of which I did not even know existed. -
This Electronic Thesis Or Dissertation Has Been Downloaded from Explore Bristol Research
This electronic thesis or dissertation has been downloaded from Explore Bristol Research, http://research-information.bristol.ac.uk Author: Vagopoulou, Evaggelia Title: Cultural tradition and contemporary thought in Iannis Xenakis's vocal works General rights Access to the thesis is subject to the Creative Commons Attribution - NonCommercial-No Derivatives 4.0 International Public License. A copy of this may be found at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode This license sets out your rights and the restrictions that apply to your access to the thesis so it is important you read this before proceeding. Take down policy Some pages of this thesis may have been removed for copyright restrictions prior to having it been deposited in Explore Bristol Research. However, if you have discovered material within the thesis that you consider to be unlawful e.g. breaches of copyright (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please contact [email protected] and include the following information in your message: •Your contact details •Bibliographic details for the item, including a URL •An outline nature of the complaint Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. Cultural Tradition and Contemporary Thought in lannis Xenakis's Vocal Works Volume I: Thesis Text Evaggelia Vagopoulou A dissertation submitted to the University of Bristol in accordancewith the degree requirements of the of Doctor of Philosophy in the Faculty of Arts, Music Department. -
Monte Carlo Sampling Methods
[1] Monte Carlo Sampling Methods Jasmina L. Vujic Nuclear Engineering Department University of California, Berkeley Email: [email protected] phone: (510) 643-8085 fax: (510) 643-9685 UCBNE, J. Vujic [2] Monte Carlo Monte Carlo is a computational technique based on constructing a random process for a problem and carrying out a NUMERICAL EXPERIMENT by N-fold sampling from a random sequence of numbers with a PRESCRIBED probability distribution. x - random variable N 1 xˆ = ---- x N∑ i i = 1 Xˆ - the estimated or sample mean of x x - the expectation or true mean value of x If a problem can be given a PROBABILISTIC interpretation, then it can be modeled using RANDOM NUMBERS. UCBNE, J. Vujic [3] Monte Carlo • Monte Carlo techniques came from the complicated diffusion problems that were encountered in the early work on atomic energy. • 1772 Compte de Bufon - earliest documented use of random sampling to solve a mathematical problem. • 1786 Laplace suggested that π could be evaluated by random sampling. • Lord Kelvin used random sampling to aid in evaluating time integrals associated with the kinetic theory of gases. • Enrico Fermi was among the first to apply random sampling methods to study neutron moderation in Rome. • 1947 Fermi, John von Neuman, Stan Frankel, Nicholas Metropolis, Stan Ulam and others developed computer-oriented Monte Carlo methods at Los Alamos to trace neutrons through fissionable materials UCBNE, J. Vujic Monte Carlo [4] Monte Carlo methods can be used to solve: a) The problems that are stochastic (probabilistic) by nature: - particle transport, - telephone and other communication systems, - population studies based on the statistics of survival and reproduction. -
Exploring Xenakis Performance, Practice, Philosophy
Exploring Xenakis Performance, Practice, Philosophy Edited by Alfia Nakipbekova University of Leeds, UK Series in Music Copyright © 2019 Vernon Press, an imprint of Vernon Art and Science Inc, on behalf of the author. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Vernon Art and Science Inc. www.vernonpress.com In the Americas: In the rest of the world: Vernon Press Vernon Press 1000 N West Street, C/Sancti Espiritu 17, Suite 1200, Wilmington, Malaga, 29006 Delaware 19801 Spain United States Series in Music Library of Congress Control Number: 2019931087 ISBN: 978-1-62273-323-1 Cover design by Vernon Press. Cover image: Photo of Iannis Xenakis courtesy of Mâkhi Xenakis. Product and company names mentioned in this work are the trademarks of their respective owners. While every care has been taken in preparing this work, neither the authors nor Vernon Art and Science Inc. may be held responsible for any loss or damage caused or alleged to be caused directly or indirectly by the information contained in it. Every effort has been made to trace all copyright holders, but if any have been inadvertently overlooked the publisher will be pleased to include any necessary credits in any subsequent reprint or edition. Table of contents Introduction v Alfia Nakipbekova Part I - Xenakis and the avant-garde 1 Chapter 1 ‘Xenakis, not Gounod’: Xenakis, the avant garde, and May ’68 3 Alannah Marie Halay and Michael D. -
Iannis Xenakis, Roberta Brown, John Rahn Source: Perspectives of New Music, Vol
Xenakis on Xenakis Author(s): Iannis Xenakis, Roberta Brown, John Rahn Source: Perspectives of New Music, Vol. 25, No. 1/2, 25th Anniversary Issue (Winter - Summer, 1987), pp. 16-63 Published by: Perspectives of New Music Stable URL: http://www.jstor.org/stable/833091 Accessed: 29/04/2009 05:06 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=pnm. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit organization founded in 1995 to build trusted digital archives for scholarship. We work with the scholarly community to preserve their work and the materials they rely upon, and to build a common research platform that promotes the discovery and use of these resources. For more information about JSTOR, please contact [email protected]. Perspectives of New Music is collaborating with JSTOR to digitize, preserve and extend access to Perspectives of New Music. http://www.jstor.org XENAKIS ON XENAKIS 47W/ IANNIS XENAKIS INTRODUCTION ITSTBECAUSE he wasborn in Greece?That he wentthrough the doorsof the Poly- technicUniversity before those of the Conservatory?That he thoughtas an architect beforehe heardas a musician?Iannis Xenakis occupies an extraodinaryplace in the musicof our time. -
Random Numbers and Stochastic Simulation
Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Scientific Computing: An Introductory Survey Chapter 13 – Random Numbers and Stochastic Simulation Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c 2002. Reproduction permitted for noncommercial, educational use only. Michael T. Heath Scientific Computing 1 / 17 Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Stochastic Simulation Stochastic simulation mimics or replicates behavior of system by exploiting randomness to obtain statistical sample of possible outcomes Because of randomness involved, simulation methods are also known as Monte Carlo methods Such methods are useful for studying Nondeterministic (stochastic) processes Deterministic systems that are too complicated to model analytically Deterministic problems whose high dimensionality makes standard discretizations infeasible (e.g., Monte Carlo integration) < interactive example > < interactive example > Michael T. Heath Scientific Computing 2 / 17 Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Stochastic Simulation, continued Two main requirements for using stochastic simulation methods are Knowledge of relevant probability distributions Supply of random numbers for making random choices Knowledge of relevant probability distributions depends on theoretical or empirical information about physical system being simulated By simulating large number of trials, probability -
Nash Q-Learning for General-Sum Stochastic Games
Journal of Machine Learning Research 4 (2003) 1039-1069 Submitted 11/01; Revised 10/02; Published 11/03 Nash Q-Learning for General-Sum Stochastic Games Junling Hu [email protected] Talkai Research 843 Roble Ave., 2 Menlo Park, CA 94025, USA Michael P. Wellman [email protected] Artificial Intelligence Laboratory University of Michigan Ann Arbor, MI 48109-2110, USA Editor: Craig Boutilier Abstract We extend Q-learning to a noncooperative multiagent context, using the framework of general- sum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restric- tions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learn- ing performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance. -
Stochastic Versus Uncertainty Modeling
Introduction Analytical Solutions The Cloud Model Conclusions Stochastic versus Uncertainty Modeling Petra Friederichs, Michael Weniger, Sabrina Bentzien, Andreas Hense Meteorological Institute, University of Bonn Dynamics and Statistic in Weather and Climate Dresden, July 29-31 2009 P.Friederichs, M.Weniger, S.Bentzien, A.Hense Stochastic versus Uncertainty Modeling 1 / 21 Introduction Analytical Solutions Motivation The Cloud Model Outline Conclusions Uncertainty Modeling I Initial conditions { aleatoric uncertainty Ensemble with perturbed initial conditions I Model error { epistemic uncertainty Perturbed physic and/or multi model ensembles Simulations solving deterministic model equations! P.Friederichs, M.Weniger, S.Bentzien, A.Hense Stochastic versus Uncertainty Modeling 2 / 21 Introduction Analytical Solutions Motivation The Cloud Model Outline Conclusions Stochastic Modeling I Stochastic parameterization { aleatoric uncertainty Stochastic model ensemble I Initial conditions { aleatoric uncertainty Ensemble with perturbed initial conditions I Model error { epistemic uncertainty Perturbed physic and/or multi model ensembles Simulations solving stochastic model equations! P.Friederichs, M.Weniger, S.Bentzien, A.Hense Stochastic versus Uncertainty Modeling 3 / 21 Introduction Analytical Solutions Motivation The Cloud Model Outline Conclusions Outline Contrast both concepts I Analytical solutions of simple damping equation dv(t) = −µv(t)dt I Simplified, 1-dimensional, time-dependent cloud model I Problems P.Friederichs, M.Weniger, S.Bentzien, -
[email protected] Abstract
Bridges 2018 Conference Proceedings From the Cheesegrater to the Parthenon: A Musical Odyssey Terry Trickett Barbican, London, United Kingdom; [email protected] Abstract The close relationship that can exist between architecture and music was brought home to me, recently, by one particular building in the City of London. Its architects had not set out to produce architecture with a musical agenda but music can happen anyway when a building generates its own harmony, pattern, discord, repetition and silence. These were the qualities I found in the Leadenhall Building (known colloquially as the ‘Cheesegrater’) which prompted me to produce a piece of Visual Music linking the two art forms. The result, Citirama, enables me to formulate a considered view on how music and architecture can be found to coexist: how our understanding of the concept of space/time can bring an art form concerned primarily in mapping sound in time closer to a form focused on what we experience in moving through space. For a building to elicit an emotional response a chord needs to be struck inside us that delivers an inner sense of harmony. Such an experience is rare even though the synchronicity that exists between architecture and music, underpinned by mathematics and the laws of physics, was discovered long ago by the Ancient Greeks. Introduction As abstract arts, architecture and music share a common aim to create harmony, weave patterns, define spaces, move in time, promote feelings, touch the senses and act as an outward expression of an artist‘s aspirations. Such a statement must, in itself, provoke discord; surely, these aims cannot be achieved in equal measure by both art forms.