Title :
Ionic-channel signal processing using artificial neural network
Author :
Moghaddamjoo, A. Reza ; Malki, Heidar
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA
Abstract :
A specifically designed neural network is used to process ionic-channel signals. The model includes three major stages: the normalization, neural network main body and postprocessing stages. In the normalization stage, the real world signal is preprocessed and preconditioned for the use by the neural network. The main body of the model, the neural network, functions as a pattern recognizer, using a multilayered feedforward architecture along with a supervised back-propagation training algorithm. The postprocessing stage includes interpretation of the neural network outputs to establish the best estimates of the locations of step changes in the input signal
Keywords :
learning systems; neural nets; pattern recognition; signal processing; artificial neural network; input signal step change location estimations; ionic channel signal processing; multilayered feedforward architecture; neural network main body stage; neural network output interpretation; normalization stage; pattern recognizer; postprocessing stage; real world signal preconditioning; real world signal preprocessing; specifically designed neural network; supervised back-propagation training algorithm; Artificial neural networks; Clustering algorithms; Dynamic range; Neural networks; Pattern classification; Signal processing; Signal processing algorithms; Statistics; Stochastic processes; Switches;
Conference_Titel :
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location :
Portland, OR
DOI :
10.1109/ISCAS.1989.100468
