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Show 47 Neural Network Architecture 2.2.2.2 A three (an input layer, layer feedforward neural output layer) Each 2.1. hidden input connected was The basic through weighted link a input The to the to the network normalized oscillometric consisted of in Figure of hidden Section 2.2.3, layer nodes was evaluate the to varied, affect 2.2.2.3 offset The layer nodes were the of and the type Section a the For a or adaptively, each hidden continuous the The mmHg. in mmHg estimate internal an The either of threshold node the described below as the rather than was an in units applied of the hidden in binary output to the layer node functioned output above, provided not a shown as layer as inputs weighted in mmHg. was Figure 2.2. desired the was nodes simple and, as summer stated estimate of the desired blood pressure of of sum output layer. a or of the hidden offsets layer node, sigmoid nonlinearity weighted outputs attribute 187 performance. nonlinearity through which determined sigmoid nonlinearity Instead, to (dot product of the node input and weight vectors) passed through Because a arterial blood pressure. internal thresholds 2.2.2.4. inputs node. samples of network commonly characterized by are passed. are connected Transfer Function Node Nodes systolic or mean, an every described below in as on to evenly spaced in 3 waveform single output of the network yielded diastolic, shown in turn was mmHg increments of cuff pressure ranging from 10 number process of the are 60 an architecture single output layer amplitude to weighted link a layer node and each hidden layer node through and layer, designed was output data formats and input and the hidden network oscillometric amplitude waveforms. network one of |
