Artificial Neural Network
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Abstract:
Artificial neural network is the core of deep learning algorithms and the forefront of artificial intelligence. Its inspiration comes from neurons within the human brain. Artificial neural network mimics the way biological neurons transmit signals to each other. It can thus achieve the goal of learning experiences. This chapter introduces artificial neuron, perceptron and basic model of artificial neural network. Moreover, the chapter also introduces backpropagation neural network, Hopfield neural network, competitive neural network. Finally, deep neural network is introduced in the chapter. Five examples are given to show the working principle of artificial neural network. The programs for implementing the examples are also provided for better understanding the model of artificial neural network.Keywords:
Physical neural network
Perceptron
Artificial neuron
Backpropagation
Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data.An artificial neural network is a machine learning algorithm based on the concept of a human neuron.The purpose of this review is to explain the fundamental concepts of artificial neural networks.
Physical neural network
Artificial neuron
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Information processing in the human brain has always been considered as a source
of inspiration in Artificial Intelligence; in particular, it has led researchers to develop
different tools such as artificial neural networks. Recent findings in Neurophysiology
provide evidence that not only neurons but also isolated and networks of astrocytes
are responsible for processing information in the human brain. Artificial neural net-
works (ANNs) model neuron-neuron communications. Artificial neuron-glia networks
(ANGN), in addition to neuron-neuron communications, model neuron-astrocyte con-
nections. In continuation of the research on ANGNs, first we propose, and evaluate a
model of adaptive neuro fuzzy inference systems augmented with artificial astrocytes.
Then, we propose a model of ANGNs that captures the communications of astrocytes
in the brain; in this model, a network of artificial astrocytes are implemented on top
of a typical neural network. The results of the implementation of both networks show
that on certain combinations of parameter values specifying astrocytes and their con-
nections, the new networks outperform typical neural networks. This research opens
a range of possibilities for future work on designing more powerful architectures of
artificial neural networks that are based on more realistic models of the human brain.
Artificial neuron
Biological neuron model
Physical neural network
Neurophysiology
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Artificial neural network is the core of deep learning algorithms and the forefront of artificial intelligence. Its inspiration comes from neurons within the human brain. Artificial neural network mimics the way biological neurons transmit signals to each other. It can thus achieve the goal of learning experiences. This chapter introduces artificial neuron, perceptron and basic model of artificial neural network. Moreover, the chapter also introduces backpropagation neural network, Hopfield neural network, competitive neural network. Finally, deep neural network is introduced in the chapter. Five examples are given to show the working principle of artificial neural network. The programs for implementing the examples are also provided for better understanding the model of artificial neural network.
Physical neural network
Perceptron
Artificial neuron
Backpropagation
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Artificial neural network is a theory and technology which developed rapidly in recent years in the field of computer intelligence. In this paper, artificial neural network and system simulation concepts were introduced, and proposed simulation models based on dynamic neural network and system simulation model based on process neural network, and described the learning algorithm of the model.
Physical neural network
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The importance of artificial intelligence in neural networks cannot be overstated. Neural networks are complex systems that are difficult for humans to understand. By using artificial intelligence, the neural networks can be trained to recognize patterns and make predictions. Neural networks are a key part of artificial intelligence (AI). Their ability to simulate the workings of the human brain and learn from data makes them well-suited for a number of applications. However, there are still some challenges that need to be addressed in order for neural networks to be truly effective. This research study will examine the role of neural networks in AI, and discuss about some of the challenges that need to be addressed in order to become truly effectivee This research study utilizes MediaPipe to describe the importance of artificial intelligence in neural networks.
Physical neural network
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The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.
Physical neural network
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The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.
Physical neural network
Artificial neuron
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The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.
Physical neural network
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The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.
Physical neural network
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