Artificial neural networks. (English) Zbl 1530.68207
Kunze, Herb (ed.) et al., Engineering mathematics and artificial intelligence. Foundations, methods, and applications. Boca Raton, FL: CRC Press. Math. Appl.: Model. Engin. Soc. Sci., 227-244 (2024).
Summary: Artificial neural networks (ANNs) were designed based on the present understanding of their biological counterpart. An ANN is a system which serves as a fully parallel analog computer to mimic some aspect of cognition. Throughout the mid-2000s, many different architectures have been explored and have won contests related to machine learning and image recognition. This chapter discusses the architecture of the following types of neural networks: the perceptron model, feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks, and complex-valued neural networks. ANNs have been widely used in recent years, with applications such as image classification, speech recognition, and natural language processing. An ANN is a collection of artificial neurons constructed by connecting neurons with a weighted connection. CNNs are similar to the feedforward ANNs but are typically used to solve image and computer vision-related problems but have also been applied to natural language processing.
For the entire collection see [Zbl 1523.68010].
For the entire collection see [Zbl 1523.68010].
MSC:
68T05 | Learning and adaptive systems in artificial intelligence |
68T10 | Pattern recognition, speech recognition |
68T45 | Machine vision and scene understanding |
68T50 | Natural language processing |
68U10 | Computing methodologies for image processing |
92B20 | Neural networks for/in biological studies, artificial life and related topics |