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Neue Materialien für einen realitätsbezogenen Mathematikunterricht 8: ISTRON-Schriftenreihe (Realitätsbezüge im Mathematikunterricht)
by Katrin Vorhölter Martin Bracke Matthias LudwigDieser neue ISTRON-Band für die Lehrerbildung beschäftigt sich mit der Konzeption, Organisation und Betreuung von Modellierungsprojekten für Schülerinnen und Schüler. Hierbei handelt es sich um offene Fragestellungen – in vielen Fällen eingebettet in ein interdisziplinäres Umfeld – bei denen es in der Bearbeitung einen großen Freiraum gibt, sowohl die inhaltliche Ausgestaltung als auch den zeitlichen Rahmen betreffend. Die Autoren/innen sind erfahrene Mathematikdidaktiker/innen und die Projekte wurden bereits erfolgreich erprobt. Inhaltlich reichen die Themen von der Frage Wie funktionieren Animationsfilme? über Funktionsweise und Konstruktion einer Spidercam® bis hin zu Modellierung, Simulation und Bau eines Musikbrunnens. Außerdem werden die folgenden Fragestellungen adressiert, die bei der Planung von eigenen Modellierungsprojekten sehr oft auftreten: Wie kommt man an eine Problemstellung?, Welche Rolle spielt der Computereinsatz in Modellierungsprojekten?, Wie kann eine sinnvolle Betreuung der Lernenden aussehen? und Welche Chancen und Herausforderungen bieten Modellierungsprojekte mit Schülerinnen und Schülern?. Dazu kommen Erfahrungsberichte aus Perspektive der Lernenden, der Lehrkräfte als Teilnehmer solcher Projekte sowie der Projektplaner/innen und -betreuer/innen.Dies ist der siebte Band mit Neuem Material für einen realitätsbezogenen Mathematikunterricht von ISTRON, einer Gruppe von Lehrenden an Schulen und Hochschulen sowie in der Lehrerbildung tätigen Personen, der innerhalb der Reihe Realitätsbezüge im Mathematikunterricht erscheint.
Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9: ISTRON-Schriftenreihe (Realitätsbezüge im Mathematikunterricht)
by Martin Frank Christina RoeckerathErprobte Materialien mit authentischen und realen Modellierungsproblemen für den eigenen Mathematikunterricht? Dieses Buch liefert genau das. (Angehende) Lehrkräfte erhalten digitale und direkt einsetzbare Lehr- und Lernmaterialien für die Umsetzung von schülernahen Projekten zur mathematischen Modellierung. Es werden fünf Workshops zu realen Problemstellungen inklusive der zugehörigen digitalen Lernmaterialien detailliert beschrieben, die allesamt zahlreiche Anknüpfungspunkte an schulmathematische Inhalte liefern.In den fünf Workshops können die Schülerinnen und Schülerdie Bedeutung mathematischer Modellierung im Bereich Solarenergie erkunden,diskutieren und statistisch begründen, inwieweit der Klimawandel existiert,die Funktionsweise von Computertomographen erarbeiten,am Beispiel von Liedern ein eigenes Modell zur Datenkomprimierung entwickeln oder der Funktionsweise der Musikerkennungs-App Shazam auf den Grund gehen.Die Materialien der Workshops wurden bereits in verschiedenen Modellierungsveranstaltungen mit Schülerinnen und Schülern unterschiedlicher Jahrgangsstufen (ab Klasse 9) bearbeitet und kontinuierlich weiterentwickelt. Dieser Band liefert Hintergrundwissen zu allen fünf Workshops sowie didaktische Tipps für deren Umsetzung im Mathematikunterricht oder in fächerübergreifenden Projekten. Zugleich erhalten die Lehrkräfte Zugang zu dem digitalen Lernmaterial der Workshops. Dieses liegt auf einer Workshop-Plattform zum direkten Unterrichtseinsatz bereit. Lehrkräfte sowie Schülerinnen und Schüler können die Workshopmaterialien im Webbrowser bearbeiten.Die ZielgruppenMathematiklehrerinnen und -lehrer der SekundarstufenLehrende in der Fort- und Weiterbildung für Lehrkräfte (für Mathematiklehrkräfte)Studierende des Lehramts Mathematik ab dem 1. SemesterLehrende der Mathematik und ihrer Didaktik an Hochschulen
Neue Perspektiven auf mathematische Lehr-Lernprozesse mit digitalen Medien: Eine Auswahl grundlagenorientierter und praxisorientierter Beiträge (MINTUS – Beiträge zur mathematisch-naturwissenschaftlichen Bildung)
by Frederik Dilling Felicitas Pielsticker Ingo WitzkeDer Band stellt eine mathematikdidaktische Zusammenschau zum Einsatz digitaler Medien und Werkzeuge im Mathematikunterricht sowie in der Lehramtsausbildung Mathematik dar. Enthalten sind sowohl grundlagenorientierte Beiträge als auch reflektierte Praxisbeiträge. Die Autor*innen des Sammelwerks teilen eine positive Grundeinstellung zu den Möglichkeiten, die digitale Werkzeuge und Medien für den Mathematikunterricht entfalten können, wägen aber jeweils aus mathematikdidaktischer Perspektive kritisch ab, wann, wo und wie ein Einsatz einen fachinhaltlichen und fachdidaktischen Mehrwert ermöglichen kann.
Neue Wege im mathematischen Unterricht: Auf den Spuren Mathilde Vaertings (Paderborner Beiträge zur Didaktik der Mathematik)
by Gerda WerthMathilde Vaerting (1884 – 1977) möchte den Mathematikunterricht ihrer Zeit radikal verändern und mit ihrer Methode der „Selbständigkeitsprobe“ einen Weg aufzeigen, Schüler*innen durch geeignete kognitive Anregung zu eigenständigem Denken zu motivieren. Ihre „Neue[n] Wege im mathematischen Unterricht“ aus 1921 schließen dabei explizit Mädchen ein, obwohl diesen, nachdem sie seit 1908 endlich auch Mathematik an Schulen lernen durften, die Begabung für dieses Fach vielfach abgesprochen wurde. Das Buch arbeitet ihre didaktischen Konzepte sowie die schulischen und curricularen Rahmenbedingungen auf, auch in Bezug auf die Lehrerinnenbildung der damaligen Zeit.
Neural Approaches to Dynamics of Signal Exchanges (Smart Innovation, Systems and Technologies #151)
by Anna Esposito Marcos Faundez-Zanuy Francesco Carlo Morabito Eros PaseroThe book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.
Neural Approximations for Optimal Control and Decision (Communications and Control Engineering)
by Riccardo Zoppoli Marcello Sanguineti Giorgio Gnecco Thomas ParisiniNeural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors’ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
Neural Computing for Advanced Applications: 5th International Conference, NCAA 2024, Guilin, China, July 5–7, 2024, Proceedings, Part I (Communications in Computer and Information Science #2181)
by Haijun Zhang Xianxian Li Tianyong Hao Weizhi Meng Zhou Wu Qian HeThis book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024. The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections: Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Computer vision, and their engineering applications. Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks. Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications; Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security.
Neural Computing for Advanced Applications: 5th International Conference, NCAA 2024, Guilin, China, July 5–7, 2024, Proceedings, Part II (Communications in Computer and Information Science #2182)
by Haijun Zhang Xianxian Li Tianyong Hao Weizhi Meng Zhou Wu Qian HeThis book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024. The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections: Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Computer vision, and their engineering applications. Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks. Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications; Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security.
Neural Computing for Advanced Applications: 5th International Conference, NCAA 2024, Guilin, China, July 5–7, 2024, Proceedings, Part III (Communications in Computer and Information Science #2183)
by Haijun Zhang Xianxian Li Tianyong Hao Weizhi Meng Zhou Wu Qian HeThis book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024. The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections: Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Computer vision, and their engineering applications. Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks. Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications; Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part I (Lecture Notes in Computer Science #15286)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part III (Communications in Computer and Information Science #2284)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part IX (Communications in Computer and Information Science #2290)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part VIII (Communications in Computer and Information Science #2289)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part VIII (Lecture Notes in Computer Science #15293)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part X (Communications in Computer and Information Science #2291)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part XI (Lecture Notes in Computer Science #15296)
by Kevin Wong M. Tanveer Mufti Mahmud Maryam Doborjeh Andrew Chi Sing Leung Zohreh DoborjehThe eleven-volume set LNCS 15286-15295 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.
Neural Modeling of Speech Processing and Speech Learning: An Introduction
by Bernd J. Kröger Trevor BekolayThis book explores the processes of spoken language production and perception from a neurobiological perspective. After presenting the basics of speech processing and speech acquisition, a neurobiologically-inspired and computer-implemented neural model is described, which simulates the neural processes of speech processing and speech acquisition. This book is an introduction to the field and aimed at students and scientists in neuroscience, computer science, medicine, psychology and linguistics.
Neural Network Analysis, Architectures and Applications
by Antony BrowneNeural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
Neural Network Methods for Dynamic Equations on Time Scales (SpringerBriefs in Applied Sciences and Technology)
by Svetlin GeorgievThis book aims to handle dynamic equations on time scales using artificial neural network (ANN). Basic facts and methods for ANN modeling are considered. The multilayer artificial neural network (ANN) model is introduced for solving of dynamic equations on arbitrary time scales. A multilayer ANN model with one input layer containing a single node, a hidden layer with m nodes, and one output node are investigated. The feed-forward neural network model and unsupervised error back-propagation algorithm are developed. Modification of network parameters is done without the use of any optimization technique. The regression-based neural network (RBNN) model is introduced for solving dynamic equations on arbitrary time scales. The RBNN trial solution of dynamic equations is obtained by using the RBNN model for single input and single output system. A variety of initial and boundary value problems are solved. The Chebyshev neural network (ChNN) model and Levendre neural network model are developed. The ChNN trial solution of dynamic equations is obtained by using the ChNN model for single input and single output system. This book is addressed to a wide audience of specialists such as mathematicians, physicists, engineers, and biologists. It can be used as a textbook at the graduate level and as a reference book for several disciplines.
Neural Network Perspectives on Cognition and Adaptive Robotics
by A. BrowneFeaturing an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to the modeling of human cognition and language using neural computing techniques. It also describes how adaptive robotic systems can be produced using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a different perspective.
Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems
by Heidar A. Talebi Farzaneh Abdollahi Kasra EsfandiariThe focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Neural Networks and Statistical Learning
by Ke-Lin Du M. N. SwamyThis book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing.Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:• multilayer perceptron;• the Hopfield network;• associative memory models;• clustering models and algorithms;• t he radial basis function network;• recurrent neural networks;• nonnegative matrix factorization;• independent component analysis;•probabilistic and Bayesian networks; and• fuzzy sets and logic.Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Neural Networks with TensorFlow and Keras: Training, Generative Models, and Reinforcement Learning
by Philip HuaExplore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models Apply machine learning and neural network techniques in various professional scenarios Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques
Neural-Based Orthogonal Data Fitting
by Giansalvo Cirrincione Maurizio CirrincioneThe presentation of a novel theory in orthogonal regressionThe literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem.The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as:Shows TLS problems and gives a sketch of their history and applicationsPresents MCA EXIN and compares it with the other existing approachesIntroduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAOOutlines the GeTLS EXIN theory for generalizing and unifying the regression problemsEstablishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problemIn dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.
Neural-Network Simulation of Strongly Correlated Quantum Systems (Springer Theses)
by Stefanie CzischekQuantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.