Analysis of Problems in Dynamics by Electronic Circuits

Ragazzini, John R.; Randall, Robert H.; Russell, Frederick A.

doi:10.1177/003754976400300309

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Between 1920 and 1950 analogous techniques dominated the field of modelling and simulation. A significant change took place when Ragazzini et al (1947) demonstrated that simulations could be done electronically. By 1967 more than 23 different programs for digital computers were available and the first graphical environment appeared, representing models in block structure.…”

Section: Modelling and Simulationmentioning

confidence: 99%

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

Winkler¹

2020

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Ziel der Arbeit ist unterschiedliche Einsatzszenarien von neuronalen Feed-Forward Netzwerken in der hybriden Modellierung zu untersuchen. Im Speziellen konzentriert sich die Arbeit auf hybride dynamische Systeme. Dabei handelt es sich um kontinuierliche Vorgänge, welche durch diskrete Ereignisse Änderung im Verhalten aufzeigen. Dementsprechend basiert die Modellierung solcher Systeme auf einer Kombination aus diskreten und kontinuierlichen Methoden. Derzeitige Standards der hybriden Modellbildung werden überblicksmäßig zusammengefasst. In der Softwareentwicklung kommen vermehrt Machine Learning Algorithmen zur Anwendung. Ein signifikanter Anteil der dabei verwendeten Methoden sind künstliche neuronale Netze, welche vor allem im Bereich der Bild-und Datenverarbeitung eingesetzt werden. Der grundlegende Aufbau sowie unterschiedliche gängige Strukturen neuronaler Netze werden eingeführt. Aufbauend auf die Gebiete Machine Learning und hybride dynamische Systeme, wird ein Framework vorgestellt, welches es ermöglicht, einzelne Elemente hybrider Modelle durch neuronale Netzwerke zu ersetzen. Drei unterschiedliche Anwendungsszenarien werden dabei untersucht: Die Approximation des dynamischen Verhaltens, die Vorhersage der diskreten Prozesse sowie der Ersatz des gesamten hybriden Systems durch neuronale Netze. Das Framework wird eingesetzt, um die drei definierten Szenarien anhand von zwei Beispielen auf Machbarkeit zu untersuchen. Es wird sich zeigen, dass die Approximation einzelner lokaler dynamischer Prozesse mittels speziellen neuronalen Netzwerken möglich ist. Auch eine Approximation des gesamten hybriden Systems durch ein neuronales Netz kann unter speziellen Voraussetzungen zum Ziel führen. Für eine gesamte Zeitreihe des Systemzustandes werden bereits etablierte Methoden mit Hilfe des Frameworks charakterisiert. Im Bezug auf diskrete Ereignisse in hybriden Systemen kann gezeigt werden, dass sich neuronale Netze für die Klassifikation des Systemzustandes, hinsichtlich des Auftretens eines Events, nicht eignen. Abseits von Anwendungen in der Modellierung hybrider Systeme kann das Framework eingesetzt werden, um Feed-Forward Netzwerke zu charakterisieren.

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Section: Modelling and Simulationmentioning

confidence: 99%

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

Winkler¹

2020

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“…Arithmetics We can divide the arithmetic operations into three fundamental categories, that are addressed by specific types of electronic circuits: (a) multiplication by a scalar; (b) addition/sum; (c) multiplication of signals. In the case of (a) and (b), a fundamental component is the operational amplifier [18]. This component allows a gain to be applied to the signal, and facilitates both multiplication and addition to be implemented.…”

Section: Computing Elementsmentioning

confidence: 99%

The Analogue Computer as a Voltage-Controlled Synthesiser

Lazzarini,

Timoney

2019

Preprint

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This paper re-appraises the role of analogue computers within electronic and computer music and provides some pointers to future areas of research. It begins by introducing the idea of analogue computing and placing in the context of sound and music applications. This is followed by a brief examination of the classic constituents of an analogue computer, contrasting these with the typical modular voltage-controlled synthesiser. Two examples are presented, leading to a discussion on some parallels between these two technologies. This is followed by an examination of the current state-of-the-art in analogue computation and its prospects for applications in computer and electronic music.

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“…The technology of the ubiquitous operational amplifier -now an indispensable component of every control system -was originally invented and perfected for analog computing. Columbia professor John Ragazzini invented the term "operational amplifier" and described the state-of-the-art electronic analog techniques in his landmark paper [15].…”

Section: An Overview Of Mathematical Approaches For Modelling Cdsmentioning

confidence: 99%

A Critical Overview of Modelling and Simulating Methods for Complex Dynamic Systems

Groumpos¹

2017

SNE

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Nowadays, practical dynamic systems have become more and more complex. The concept of Complex Dynamic Systems (CDS) arises in many scientific fields, technological areas and everyday's problems. The fundamental basics of complex dynamic systems are briefly presented and discussed. Modelling and simulating complex dynamic systems is a very difficult and challenging task. Different modelling approaches basically focus on the interaction between (microscopic) subsystems and the emergence of new qualities at the (macroscopic) system level. However these models are not sufficient to describe the dynamic behavior of today's systems. Similarly despite the numerous Simulation methods and large number of simulation software tools many of today's challenging problems cannot be studied appropriately and provide satisfactory answers. It seems that the uncertainty and fuzziness been inevitably present on these systems make the problems more difficult. The new models of Fuzzy Cognitive Maps are proposed as a new modelling approach for CDS. The need for new advanced models and new simulation methods is apparent taking into consideration the presence of human nature in all system processes, as well as the fuzzy and uncertaint environment. Some conclusions and future research directions are provided.

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Analysis of Problems in Dynamics by Electronic Circuits

Cited by 5 publications

References 4 publications

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

The Analogue Computer as a Voltage-Controlled Synthesiser

A Critical Overview of Modelling and Simulating Methods for Complex Dynamic Systems

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