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Published in:
2010 | OriginalPaper | Chapter
Artificial Neural Networks
Authors : Anupam Shukla, Ritu Tiwari, Rahul Kala
Published in: Towards Hybrid and Adaptive Computing
Publisher: Springer Berlin Heidelberg
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Artificial Neural Networks (ANN) are an inspiration from the human brain. These systems contain a large number of neurons that work in a parallel architecture. Each neuron takes its input directly from system or from other neurons. The information is processed and given to the other neurons. This is the basic phenomenon that makes possible all simple and complex problem solving ability of these networks. The chapter discusses the various models of neural networks that include multi-layer perceptron with back propagation algorithm, radial basis function networks, learning vector quantization, self organizing maps and recurrent neural networks. We discuss the basic philosophies and problem solving approach of these networks. A lot of emphasis is given on the various system parameters and their role and importance in the overall system design. We further illustrate the various limitations of the different models. This forms the motivation behind the use of hybrid systems that we present in the subsequent chapters.