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NEURAL NETWORKS AND FUZZY LOGIC IN MEDICINE by VIKRAMAN NARAYANAMOORTHI
English | 2021 | ISBN: N/A | ASIN: B09C3S5XLG | 121 pages | EPUB | 2.01 Mb
Neural network learning rules - Supervised and Unsupervised Learning, single layer networks - Perceptron- Linear seperability Single Layer and Multilayer Perception, Adaptive linear neuron (Adaline) and LMS algorithm -
Error back propagation algorithm, generalized delta rule.
INTRODUCTION
Neural networks are members of a family of computational architectures inspired by biological brains. Traditionally a neuron operates by receiving signals from other neurons through connections, called synapses. The combination of these signals, in excess of a certain threshold or activation level, will result in the neuron firing, i.e sending a signal to other neurons connected to it. Some signals act as excitations and others as inhibitions to a neuron firing which is the collective effect of the presence or absence of firings in the pattern of synaptic connections between neurons. Figure 1 shows the biological neuron.
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