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The paper presents the neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to the fuzzy neural network for the recognition. The results of numerical experiments have confirmed the very good performance of such a solution.

The paper presents the efficient training program of multilayer feedforward neural networks. It is based on the best second order optimization algorithms including variable metric and conjugate gradient as well as application of directional minimization in each step. Its efficiency is proved on the standard tests, including parity, dichotomy, logistic and two-spiral problems. The application of the algorithm to the solution of higher dimensionality problems like deconvolution, separation of sources and identification of nonlinear dynamic plant are also given and discussed. It is shown that the appropriately trained neural network can be used for the nonconventional solution of these standard signal processing tasks with satisfactory accuracy. The results of numerical experiments are included and discussed in the paper. Copyright © 1996 Elsevier Science Ltd.

The paper presents the efficient training program of multilayer feedforward neural networks. It is based on the best second order optimization algorithms, including variable metric and conjugate gradient as well as application of directional minimization in each step. The method applies the signal flow graph approach for gradient generation. The results of standard numerical tests are given. The efficiency of the program tested on many examples, including symmetry, parity, dichotomy logistic and 2-spiral problems has shown considerable speed-up over the best, already known reported results. >