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What is Artificial Neural Network (ANN)
1.
ANN
is a soft-computing tool that can learn patterns and predicts.
Learn more in: Biometric Identification System Using Neuro and Fuzzy Computational Approaches
2.
An
artificial neural network
defines a mathematical model for the simulation of a
network
of biological neurons (e.g. human nervous system). It simulates different aspects related to the behavior and capacity of the human brain, such as: intelligent information processing; distributed processing; high level of parallelism; faculty of learning, generalization and adaptation; high tolerance to inaccurate (or wrong) information.
Learn more in: Artificial Neural Networks Tutorial
3.
An
artificial neural network
(
ANN
) is a piece of a computer system programmed to replicate the way the human brain analyzes and processes information. The foundation of
artificial
intelligence (AI) solves problems that, by human or mathematical criteria, would be impossible or complicated.
ANN
s have the capability of self-learning, meaning that more evidence is needed to obtain improved outcomes.
Learn more in: Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
4.
Artificial neural network
s (
ANN
) are the pieces of a computing system designed to simulate the way the human brain analyzes and processes information.
ANN
has self-learning capabilities that enable them to produce better results.
Learn more in: Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network
5.
A computing system made up of a number of simple, highly interconnected processing elements, which mimics the biological
neural network
in order to process information by their dynamic state response to external inputs.
Learn more in: Demographic Characterization of Heart Rate Variability (HRV)
6.
A mathematical model inspired in biological
neural network
s. The units are called neurons connected in various input, hidden and output layers. For a specific stimulus (numerical data at the input layer) some neurons are activated following an activation function and producing numerical output. Thus
ANN
is trained, storing the learned model in weight matrices of the neurons. This kind of processing has demonstrated to be suitable to find nonlinear relationships in data, being more flexible in some applications than models extracted by linear decomposition techniques.
Learn more in: Automatic Classification of Impact-Echo Spectra I
7.
ANN
represents a set of algorithms which are designed for pattern recognition. It is modelled after the human brain.
Learn more in: Adaptive Neuro-Fuzzy Inference System in Agriculture
8.
It is a soft computing tool that resembles the operation of biological
neural network
of brain. The
network
trains itself with the process of learning such that it can map a test data with a known data from a given set of known dataset. According to the learning process it may be of two types: (a) supervised learning and (b) unsupervised learning.
Learn more in: Development of a Stop-Line Violation Detection System for Indian Vehicles
9.
Artificial neural network
s (
ANN
s) are a type of computing system that is inspired by biological
neural network
s present in the animal brain.
Learn more in: Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture
10.
An
artificial neural network
is a data analysis method which operation resembles a
network
of biological neurons.
ANN
s are composed of a system of nodes (equivalent to neurons of a human brain) which are interconnected by weighted links (equivalent to synapses between neurons). The outcome of the
ANN
is altered by changes of the links’ weights. The data is fed to the input layer and the result of the
network
is displayed by the output layer. The input nodes represent the independent or predictor variables that are used for predicting the dependent variables, i.e., the output neurons.
Learn more in: Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach
11.
is a mathematical model or computational model based on biological
neural network
s. It consists of an interconnected group of
artificial
neurons and processes information using a connectionist approach to computation.
Learn more in: Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products
12.
A machine learning technique that can be used to learn historical patterns and make future predictions. It works as a simulation of our brain nervous system, each node in
ANN
represents a neuron, and one or more nodes in each layer.
Learn more in: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
13.
A computational model that aims to simulate the behavior of the human brain. In order to do this, it combines the capabilities of simple computing elements, which correspond to biological neurons, highly interconnected and organized in layered structures. The
ANN
s are part of the family of the learning by example (LBE) techniques. The
ANN
can be suitably trained via examples (input-output pairs) and, after that, it learns how to generalize; then it applies what it has learned to new inputs that are not used during the training phase.
Learn more in: EM-Source Localization in Indoor Environments by Using an Artificial Neural Network Performance Assessment and Optimization
14.
An important tool to set up linkage between provided input and required output and it is developed on the basis of the set up of communicating nervous system of human being.
Learn more in: A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network
15.
Usually called
neural network
(NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological
neural network
s. A
neural network
consists of an interconnected group of
artificial
neurons, and it processes information using a connectionist approach to computation. In most cases an
ANN
is an adaptive system that changes its structure based on external or internal information that flows through the
network
during the learning phase. Modern
neural network
s are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data.
Learn more in: Computer Intelligence in Healthcare
16.
Is biologically inspired computer program designed to simulate the way in which the human brain processes information.
ANN
gathers its knowledge by detecting the patterns and relationships in data and learns (or is trained) through experience, not from programming.
Learn more in: Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
17.
A predictive computer algorithm inspired by the biology of the human brain that can learn linear and non-linear functions from data.
Artificial neural network
s are particularly useful when the complexity of the data or the modelling task makes the design of a function that maps inputs to outputs by hand impractical.
Learn more in: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
18.
An
artificial
neuron
network
(
ANN
) is a nonlinear statistical data process inspired by the structure and functions of biological neurons, used for pattern recognition and modeling of complex input-output relationships. An
ANN
“learns” (adjusts its computational parameters) as information “flows” through its node layers, based on that input and output.
Learn more in: Machine Learning in Healthcare: Introduction and Real-World Application Considerations
19.
An interconnected group of
artificial
neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. It has the ability to learn from knowledge, which is expressed through interunit connection strengths, and can make this knowledge available for use.
Learn more in: Computer-Aided Diagnosis of Cardiac Arrhythmias
20.
A basic technique in machine learning used to find a pattern or trend in a large dataset. All input data are assumed to be connected through hidden layers(s), which is similar to the concept of biological neurons.
Learn more in: The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance
21.
It is a computing system inspired by biological
neural network
.
Learn more in: Recent Studies and Research on Sickle Cell Disease: Statistical Analysis and Machine Learning Approach
22.
It is a computing model based on the structure of the human brain with many interconnected processing nodes that model input-output relationships. The model is organized in layers of nodes that interconnect to each other.
Learn more in: Convolutional Neural Network
23.
It is a computing model based on the structure of the human brain with many interconnected processing nodes that model input-output relationships. The model is organized in layers of nodes that interconnect to each other.
Learn more in: Deep Learning on Edge: Challenges and Trends
24.
A machine learning algorithm that performs “black box” non-linear fitting, similarly to the inter-connected neurons in human brain.
Learn more in: Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method
25.
ANN
s are computational
network
s composed of multiple nodes named neurons interacting with each other. The nodes can take input data and perform simple operations on the data.
Learn more in: Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence
26.
A system made up of interconnecting
artificial
neurons or nodes (usually simplified neurons) which may share some properties of biological
neural network
s. They may either be used to gain an understanding of biological
neural network
s, or for solving traditional
artificial
intelligence tasks without necessarily attempting to model a real biological system. Well known examples of
ANN
are the Hopfield, Kohonen and Cellular (CNN) models.
Learn more in: Basic Cellular Neural Networks Image Processing
27.
An interconnected group of
artificial
neurons that uses a mathematical model, or computational model, for information processing, based on a connectionist approach to computation.
Learn more in: Arrhythmia Detection and Classification Using Wavelet and ICA
28.
Neural network
is a system which works as human brain and learn from past experiences. It is mostly used to solve complex, uncertain problems.
Learn more in: Reverse Logistics Design with Neural Networks
29.
The
artificial neural network
is a data mining framework able to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Learn more in: Image Processing and Post-Data Mining Processing for Security in Industrial Applications: Security in Industry
30.
One of the computational models of the machine learning methodology.
Artificial Neural Network
simulates neurons in human brain, learns from existing patterns and fathom
artificial
intelligence problems.
Learn more in: U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression
31.
Is an algorithm that attempts to replicate the operation of the human brain through the utilization of connected neurons which are organized in layers and send information to each other.
Learn more in: Artificial Intelligence a Driver for Digital Transformation
32.
A computational model similar to the structure and functions of biological
neural network
s.
Learn more in: Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
33.
Artificial
intelligence method that is composed of a collection of highly interconnected processing units called neurons that are used together to solve a problem.
Learn more in: Intelligent Systems to Support Human Decision Making
34.
ANN
is a computing paradigm that loosely simulates cortical structures of the brain. The simplest element of
ANN
is called a processing element, or node. Soft computing techniques are used to develop different types of
ANN
models based on different processing elements.
Learn more in: Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance
35.
Artificial neural network
referred to as
neural network
, is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to the external inputs.
Learn more in: Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)
36.
Is non-parametric tool that learns from the surroundings, retains the learning and uses it subsequently.
Learn more in: Learning Aided Digital Image Compression Technique for Medical Application
37.
ANN
s are computational
network
s composed of multiple nodes named neurons interacting with each other. The nodes can take input data and perform simple operations on the data.
Learn more in: Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments
Find more terms and definitions using our
Dictionary Search
.
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