What algorithm is used to train an artificial neural network?

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What algorithm is used to train an artificial neural network?

This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD).

What is artificial neural network method?

ANNs are a type of computer program that can be ‘taught’ to emulate relationships in sets of data. Once the ANN has been ‘trained’, it can be used to predict the outcome of another new set of input data, e.g. another composite system or a different stress environment.

What are genetic algorithms in AI?

A genetic algorithm (GA) is a heuristic search algorithm used to solve search and optimization problems. This algorithm is a subset of evolutionary algorithms, which are used in computation. Genetic algorithms employ the concept of genetics and natural selection to provide solutions to problems.

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What is genetic algorithm?

Artificial Neural Network – Genetic Algorithm. Nature has always been a great source of inspiration to all mankind. Genetic Algorithms (GAs) are search-based algorithms based on the concepts of natural selection and genetics.

What is the difference between gengenetic algorithms and neural networks?

Genetic algorithms and neural networks are completely different concepts and are used to solve different problems. In this article, first, we’ll start with a short general introduction to genetic algorithms and neural networks. Then, we’ll outline some guidelines for when we should use each of these techniques using a couple of examples.

What factors need to be considered when building a genetic algorithm?

In a genetic algorithm, population size, number of generations, crossover rate, mutation rate, and probability also need to be considered when building the ANN.

What is neural network algorithms?

Neural network algorithms are developed by replicating and using the processing of the brain as a basic unit. As it has the capability of mimicking the functionality and operation of the human brain, it can do anything that a human can do.

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