| 摘要 |
PURPOSE:To learn a template waveform, to decide whether an error is within a threshold or not, and to recognize whether an input waveform is the same as the template waveform or not by varying a weight coefficient so as to decrease an error between output data and teach data. CONSTITUTION:Plural template waveforms of a nature waveform and an extrasystole waveform are selected, respectively, and stored in a data storage part 6. The template waveform is inputted to neural network, and as a result, an output waveform obtained in an output layer and a teacher signal are compared by an error deciding part 7, and each coupling coefficient is changed so that a square error decreases. By using the same waveform as the inputted template waveform for the teacher signal, an input waveform can be learned by the network. In the error deciding part 7, a square error of output data of the neural network 5 and template data stored in the data storage part 6 is calculated. The template waveform whose error becomes minimum is recognized as a waveform nearest to input data. |
