In the era of big data, analysts usually explore various statistical models or machine-learning
methods for observed data to facilitate scientific discoveries or gain predictive power …

A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical
data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-
Temporal Domains describes a comprehensive framework for the identification and analysis …

Some of the challenges to predict energy utilization has gained recognition in the residential
sector due to the significant energy consumption in recent decades. However, the modeling …

Although diagnostic medical imaging provides enormous benefits in the early detection and
accuracy diagnosis of various diseases, there are growing concerns on the potential side …

Convolutional neural networks (CNN) have led to many state-of-the-art results spanning
through various fields. However, a clear and profound theoretical understanding of the …

The time-frequency and time-scale communities have recently developed a large number of
overcomplete waveform dictionaries---stationary wavelets, wavelet packets, cosine packets …

In recent years there has been a growing interest in the study of sparse representation of
signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are …

This book provides the first comprehensive treatment of feed-forward neural networks from
the perspective of statistical pattern recognition. After introducing the basic concepts of …