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What is learning in artificial neural network? 

Artificial neural network
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Papers (10)Insight
Journal Article•DOI
Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information
Adrielle Silva, Irineu Lima Neto, Roseane M. Misságia, Marco Ceia, Abel G. Carrasquilla, Nathaly L. Archilha 
01 Jun 2015-Journal of Applied Geophysics
45 Citations
The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns.
Open access•Journal Article•DOI
An Overview of Neural Network
Mohaiminul Islam, Guorong Chen, Shangzhu Jin 
29 Jun 2019
26 Citations
The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task.
Journal Article•DOI
Knowledge Incorporation into Neural Networks From Fuzzy Rules
Yaochu Jin, Bernhard Sendhoff 
01 Dec 1999-Neural Processing Letters
26 Citations
The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability.
Open access•Journal Article•DOI
Software Development Effort and Cost Estimation: Neuro-Fuzzy Model
Divya Kashyap, Ashish Tripathi, A. K. Misra 
01 Jan 2012-IOSR Journal of Computer Engineering
10 Citations
Artificial Neural Network has the ability to learn from previous data.
Open access•Journal Article•DOI
Application of artificial neural networks to predict the deflections of reinforced concrete beams
Mateusz Kaczmarek, Agnieszka Szymanska 
01 Jun 2016-Studia Geotechnica et Mechanica
16 Citations
Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis.
Open access•Posted Content
Continual learning: A comparative study on how to defy forgetting in classification tasks.
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory G. Slabaugh, Tinne Tuytelaars 
18 Sep 2019
187 Citations
Artificial neural networks thrive in solving the classification problem for a particular rigid task, where the network resembles a static entity of knowledge, acquired through generalized learning behaviour from a distinct training phase.
Proceedings Article•DOI
Characterization of artificial neural network algorithms
T. Baker, Dan Hammerstrom 
08 May 1989
26 Citations
One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model.
Open access•Journal Article
A virtual laboratory for neural network controlled DC motors based on a DC-DC buck converter
Okan Bingöl, Serdar Paçaci 
01 Jan 2012-International Journal of Engineering Education
38 Citations
This training set can also guarantee effective learning andcomprehension of Artificial Neural Networks (ANN).
Journal Article•DOI
An educational tool for artificial neural networks
Omer Deperlioglu, Utku Kose 
01 May 2011-Computers & Electrical Engineering
32 Citations
By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.
Open access•Posted Content
A differentiable measure of pointwise shared information.
Abdullah Makkeh, Aaron J. Gutknecht, Michael Wibral 
09 Feb 2020
8 Citations
Thus, it lends itself for example to local learning in artificial neural networks.

Related Questions

What is learning?5 answersLearning is a process of acquiring knowledge, improving skills, developing attitudes, and producing personality through interaction with the environment and learning resources. It involves a relatively permanent change in behavior as a result of practice or experience. Learning can occur through rote memorization or meaningful understanding. It allows individuals to adapt to their environments within a lifetime and plays a crucial role in individual adaptation. Animals learn to filter out irrelevant stimuli, enhance responses to relevant stimuli, and associate behaviors with consequences. Learning can support practical activities such as finding food, migrating, and recognizing mates. Observational learning in social settings can lead to behavioral change within a group. Learning can be facilitated through interactive and enjoyable methods, such as using games and technology. Overall, learning is a dynamic process that involves acquiring knowledge, developing skills, and shaping behavior through various interactions and experiences.
What is the definition of learning?5 answersLearning can be defined as the process of acquiring knowledge, skills, and behaviors through study, experience, or teaching. It involves a relatively permanent change in behavior based on an individual's interactional experience with their environment. Learning is an active and participative process that requires individuals to engage positively with their own learning through study. It is a form of personal adaptation that occurs within the lifespan of an individual to enhance their survival and well-being. Learning is influenced by the individual's prior experiences, interactions with more experienced individuals, and the environment. It is not limited to cognitive aspects but also includes affective and psychomotor aspects. Learning is a continuous process that enables individuals to achieve independence and adapt to various environments. Utilizing technology, such as games and interactive media, can make learning more enjoyable and effective.
What is neural networks ?3 answersNeural networks are a class of artificial intelligence/machine learning tools that are inspired by the structure and functioning of the human brain. They consist of interconnected processing elements that can perform complex data processing tasks. Neural networks have gained significant attention and have been successfully applied in various fields such as quantum chemistry, handwriting recognition, robotics, and data mining. They are capable of pattern recognition and can learn from examples to perform classification and discover new trends in data. Different types of neural networks, including multi-layer perceptron (MLP), convolutional neural networks (CNN), and graph neural networks, have been developed with their own advantages and disadvantages. Neural networks have revolutionized machine learning and have achieved remarkable success in speech recognition, computer vision, industrial inspection, and national defense.
What is learning in psychology?3 answersLearning in psychology refers to the process by which behavior is modified through practice and training. It is influenced by various factors such as environment, motivation, health, emotions, maturation, and aging. Learning involves obtaining, presenting, and transforming information into knowledge, which then guides human attitudes and behavior. It is a process of active interaction with the environment that leads to relatively permanent changes in knowledge, understanding, skills, and attitudes. Learning is a cognitive-psychological process that occurs through the interaction of an animal with its environment, resulting in a modification of subsequent behavior. The ability to learn is considered one of the most important features of intelligence, and it is a process through which knowledge and abilities are acquired through experience. Learning is not limited to academic subjects but also includes skills, interests, perceptions, and habits, which are influenced by internal and external factors.
What is artificial neural networks?5 answersArtificial neural networks (ANNs) are computational methods that aim to mimic the behavior of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. The power of ANNs lies in their ability to learn tasks by approximating any computable function. They have been successfully applied in various fields such as computer vision, robotics, speech recognition, and natural language processing. ANNs have shown excellent aptitude in learning the relationships between input and output data without prior assumptions about the statistical distribution. However, ANNs have limitations in terms of explainability and global approximation functions. Therefore, there is a need for research in novel approaches for local approximators that can outperform ANNs in terms of accuracy, computational expense, and explainability.
What is artificial neural network ?4 answersAn artificial neural network (ANN) is an information processing system that mimics the working principles of the human brain. It is composed of interconnected processing elements called neurons, which work together to solve specific problems. ANNs learn from examples through a learning process, where the synaptic connections between neurons are changed. This allows the network to adapt and improve its performance. ANNs have been successfully used in various fields such as pattern recognition, data classification, computer vision, robotics, speech recognition, and natural language processing. They can approximate any computable function and are trained using labeled training data and algorithms like back-propagation of errors. ANNs can solve prediction and classification problems and adjust the connection weights between neurons to achieve accurate results.

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