Basic structure of a neural network
Matteo PasquinelliRelated papers
In Shortly about Neural Networks
A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term "neural network" refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network "learns" through repeated adjustments of these weights.
Neural networks for control systems
IEEE Transactions on Neural Networks, 1990
Neural network formalism
Neural networks are defined using only elementary concepts from set theory, without the usual connectionistic graphs. The typical neural diagrams are derived from these definitions. This approach provides mathematical techniques and insight to develop theory and applications of neural networks. 8 pages.
Understanding of neural networks
This little part of the book contain the basic information of what is a neural network for those who whish to understand neural networks, I hope that this will help you
BASICS OF ARTIFICIAL NEURAL NETWORK
A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons
For neural networks, function determines form
Neural Networks, 1993
Basic structure of a neural network
The general feedback affects the function and logic of each node
(or its ‘weight’), that is the way the node computes: it is information
turning into logic. By modifying the node threshold the control
feedback can change an OR gate into an AND gate, for example.
TECHNO-LOGICAL FORMS
Each network node is a transmission node but also a
computation node, a logic gate, a little operator or Turing
machine. Each node is both information and function, or
logic.
Adaptation
Learning
Reinforcement
Backpropagation
Weight adjustment
ƒ
Scansion
ƒ
0
Logic gate
1
Feedback loop
Control Feedback
ƒ
The control feedback that performs
weight adjustment or backpropagation
can be an equation, an algorithm or
even (ideally) a human operator.
1
environment
sensors
1
1
0
Network
INPUT
01010
image
sound
The neural network idea is the composition of four technological forms: 1) scansion, that is discretisation or digitisation
of analogue inputs since the age of radio, TV, etc.; 2) logic
gate, that can be encoded into valves, transistors or
microchips; 3) feedback loop, or the basic concept of
cybernetics; 4) network, actually inspired here by biological
neurons.
Neural network is a cybernetic network in which a general
feedback loop affects each individual node. In this sense it is
the most adaptive architecture (whose topology opens to a
true combinatory art that goes beyond the linear logic that is
expected from a classic Turing machine: see the topologies of
Recurrent Neural Network and Long Short-Term Memory NN).
OUTPUT
0
1
INPUT
LAYER
The input layer of a neural network is sometimes
called retina (since the first Perceptron) even if
it does not compute visual data.
01010
pattern recognition
classification
HIDDEN
LAYER
OUTPUT
LAYER
The neural network is constructed as a network in which
information is pipelined and distilled in higher forms of
abstraction. It is in fact an arborescent network that
sustain a hierarchical cone of more abstract features.
Diagram: Prof. Matteo Pasquinelli with Lukas Rehm 2017
Related papers
[Recenzja]: Marek G. Zieliński, Archiwum Fary Chełmińskiej, Wydawnictwo Uniwersytetu Kazimierza Wielkiego, Bydgoszcz 2019, ss. 487, 1 nlb., 86 il
Archiwa, Biblioteki i Muzea Kościelne, 2020
Военната политика на Филип ІІ Македонски и колонизацията в Югозападна Тракия / THE MILITARY POLICY OF PHILIP II OF MACEDONIA AND THE COLONIZATION IN SOUTHWESTERN THRACE
Пловдивски исторически форум, VІ, кн. 1, 5-23, 2022
The Cuming Spectacle
Sophy Rickett (ed.), ELEPHANT ATLAS, London: Studio by the Elephant, UAL: LCC, 2018
Οι παλίμψηστοι κώδικες της Εθνικής Βιβλιοθήκης της Ελλάδος. Προκαταρκτικά συμπεράσματα μιας έρευνας
2003
Synthesis and In Situ Modification of Hierarchical SAPO-34 by PEG with Different Molecular Weights; Application in MTO Process
arXiv (Cornell University), 2017
Deformation quantization of the Heisenberg group
Communications in Mathematical Physics, 1995
Pharmacological Investigation of Laurus nobilis Extract in Wistar Rats towards Anti-Hemorrhoidal Potential
In Silico Profiling and Effect of Piperine and Lignan on Ovariectomy-Induced Osteoporosis in Female Rats, 2024
Functionally Graded Sandwich Circular Plate of Non-Uniform Varying Thickness with Homogenous Core Resting on Elastic Foundation: Investigation on Bending via Differential Quadrature Method
American Journal of Mechanical Engineering, 2019
Vínculo de Comunicación Alumno-Maestro en el Aula
Razon Y Palabra, 2006
Addition of Saturated Fatty Acids to Biodegradable Films: Effect on the Crystallinity and Viscoelastic Characteristics
Journal of Polymers and the Environment, 2012
Factors influencing endothelial cell density of corneas for transplantation
Cell and Tissue Banking, 2020
Pubertal development of the first cohort of young adults conceived by in vitro fertilization in the United States
Fertility and Sterility, 2011