scholarly journals AI Methods in Algorithmic Composition: A Comprehensive Survey

2013 ◽  
Vol 48 ◽  
pp. 513-582 ◽  
Author(s):  
J.D. Fernandez ◽  
F. Vico
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Constraint Programming ◽  
Music Composition ◽  
Probabilistic Methods ◽  
Computational Techniques ◽  
Algorithmic Composition ◽  
Rule Based ◽  
Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

scholarly journals Neural Networks and Fault Probability Evaluation for Diagnosis Issues

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yahia Kourd ◽  
Dimitri Lefebvre ◽  
Noureddine Guersi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Probabilistic Methods ◽  
Fault Detection And Isolation ◽  
Fault Probability ◽  
Confidence Factor ◽  
Probability Evaluation ◽  
Quantitative Indicators ◽  
Probabilistic Criteria

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

scholarly journals Studies of Manufacturing Process Control: A Technological Transfer of Soft Computing

2008 ◽  
pp. 31-37
Author(s):  
J. P. Panda ◽  
R. N. Satpathy
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Technology Transfer ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Manufacturing Processes ◽  
Technological Transfer ◽  
Manufacturing Process Control

The field of soft computing embraces several techniques that have been inspired by nature but are mathematical. These techniques are artificial neural networks, fuzzy logic and evolutionary algorithms. Often these techniques are considered part of artificial intelligence, however the name artificial intelligence is more properly given to techniques which try to capture and emulate biological intelligence, such as expert systems and thinking computers. This paper focuses on the technology transfer issues and solutions when using soft computing for off line control of manufacturing processes. This paper will discuss each of these three techniques – neural networks, fuzzy logic and evolutionary algorithms - in turn and how they might be used in manufacturing. The kind of problems these techniques are best suited for will be defined, and competing techniques will be compared and contrasted.

Pervasive Computing for Efficient Energy

2011 ◽  
pp. 584-601
Author(s):  
Mária Bieliková ◽  
Marián Hönsch ◽  
Michal Kompan ◽  
Jakub Šimko ◽  
Dušan Zeleník
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Consumption ◽  
Power Plants ◽  
Clustering Algorithms ◽  
Energy Reduction ◽  
Prototype System ◽  
Effective Energy ◽  
Rule Based ◽  
Efficient Energy

Increasing energy consumption requires our attention. Resources are exhaustible, so building new power plants is not the only solution. Since residential expenditure is of major parts of overall consumption, concept of intelligent household has potential to participate on energy usage optimization. In this chapter, we concentrate on software methods, which based on inputs gained from an environment monitor, analyze and consequently reduce non-effective energy consumption. We gave a shape to this concept by description of real prototype system called ECM (Energy Consumption Manager). Besides active energy reduction, the ECM system also has an educative function. User-system interaction is designed to teach the user how to use (electric, in case of our prototype) energy effectively. Methods for the analysis are based on artificial intelligence and information systems fields (neural networks, clustering algorithms, rule-based systems, personalization and adaptation of user interface). The system goes further and gains more effectiveness by exchange of data, related to consumption and appliance behaviour, between households.

Vox Populi: An Interactive Evolutionary System for Algorithmic Music Composition

2000 ◽  
Vol 10 ◽  
pp. 49-54 ◽  
Author(s):  
Artemis Moroni ◽  
Jônatas Manzolli ◽  
Fernando Von Zuben ◽  
Ricardo Gudwin
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Music Composition ◽  
Sound Synthesis ◽  
Physical Factors ◽  
Algorithmic Composition ◽  
Evolutionary System ◽  
Algorithmic Music ◽  
Fitness Criterion ◽  
Algorithmic Music Composition

While recent techniques of digital sound synthesis have put numerous new sounds on the musician's desktop, several artificial-intelligence (AI) techniques have also been applied to algorithmic composition. This article introduces Vox Populi, a system based on evolutionary computation techniques for composing music in real time. In Vox Populi, a population of chords codified according to MIDI protocol evolves through the application of genetic algorithms to maximize a fitness criterion based on physical factors relevant to music. Graphical controls allow the user to manipulate fitness and sound attributes.

scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

scholarly journals Efficient Algorithmic Music Composition Using Neural Networks

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Imad Rahal ◽  
Ryan Strelow ◽  
Jeremy Iverson
Keyword(s):  
Neural Networks ◽  
Music Composition ◽  
Complex Model ◽  
Special Importance ◽  
Algorithmic Composition ◽  
Multiple Voices ◽  
Music Research ◽  
Composition Process ◽  
Music Piece ◽  
Simple Models

Creating aesthetically pleasing music via algorithmic composition has continually been an ambitious goal of music research. Memory-based neural networks have shown to be particularly suited for this type of sequential learning. Music scores data is commonly used to represent different music features–such as durations and pitches–which when combined, make up the entirety of a music piece. As more music features are integrated into the music composition process, the space of labels required to represent possible feature combinations in a neural network grows significantly and rather quickly making the process computationally challenging, to say the least. This consideration bears special importance in situations with polyphonic pieces, where additional features such as harmonies and multiple voices are present. This research highlights the potential benefits of feature separation in music composition from music scores data. More specifically, we demonstrate the effectiveness of neural networks for automated music composition by learning music features separately; we start by creating separate simple models, one for each desired music feature, and then combine results from the simple models to compose new music. This is in contrast to the common practice of employing a single complex model trained over multiple features simultaneously. Case study evaluation results show significant time savings for our proposed approach with similar music “quality” compared to the complex model.

scholarly journals CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

2005 ◽  
Vol 24 ◽  
pp. 1-48 ◽  
Author(s):  
D. Ortiz-Boyer ◽  
C. Hervás-Martínez ◽  
N. García-Pedrajas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Statistical Tests ◽  
Search Process ◽  
Crossover Operator ◽  
Exploration And Exploitation ◽  
Standard Methods ◽  
Population Distributions ◽  
Proposed Model

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.

Rule-Based and Genetic Algorithm for Automatic Gamelan Music Composition

2017 ◽  
Vol 10 (3) ◽  
pp. 202
Author(s):  
Khafiizh Hastuti ◽  
Azhari Azhari ◽  
Aina Musdholifah ◽  
Rahayu Supanggah
Keyword(s):  
Genetic Algorithm ◽  
Music Composition ◽  
Rule Based

Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

2020 ◽  
pp. 27-28
Author(s):  
A.B. Movsisyan ◽  
◽  
A.V. Kuroyedov ◽  
G.A. Ostapenko ◽  
S.V. Podvigin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Modern Method ◽  
Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

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