Abstract
Fundamental considerations of Artificial Neural Network is described in this chapter. Initially, the analogy of artificial neuron with the biological neuron is explained along with a description of the commonly used activation functions. Then, two basic ANN learning paradigms namely supervised and unsupervised learning are described. A brief note on prediction and classification using ANN is given next. Finally, primary ANN topologies like Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Probabilistic Neural Network (PNN), Learning Vector Quantization (LVQ), and Self-Organizing Map (SOM) are explained theoretically which are extensively used in the work described throughout the book.
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Sarma, M., Sarma, K.K. (2014). Fundamental Considerations of ANN. In: Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework. Studies in Computational Intelligence, vol 550. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1862-3_3
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DOI: https://doi.org/10.1007/978-81-322-1862-3_3
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