Nonlinear biomedical signal processing. Volume 1, Fuzzy logic, neural networks, and new algorithms /

Algorithms.

nonstationary nature. These algorithms are based on a number of advanced techniques including optimal spatial

classification that significantly outperform algorithms based on classical descriptions of signals

discovering new ones. Moreover, machine-learning methods include popular deep learning algorithms and are used

communications systems and useful signal processing algorithms. Random Processes: Filtering, Estimation, and

perceptron; • the Hopfield network; • associative memory models; • clustering models and algorithms; • t he

algorithmic analysis; Applications in salient object detection and eye fixation prediction. Students majoring

simulation algorithms of stationary random signals with a given power spectrum density; · Complementary

Unsupervised Clustering Algorithm -- 9.2 Bayes Classifier -- 9.3 Support Vector Machine -- 9.4 Neural Networks

Algorithms for Wavelet Transform Computation (O. Rioul & P. Duhamel) -- Analysis of Cellular Vibrations in

, Neuro-Fuzzy Networks, and Genetic Algorithms (Giovanni Magenes, Francesco Lunghi, and Stefano Ramat) -- 22