Rosa J.L.G. Artificial Neural Networks: Models and Applications

New York: ITexLi, 2016. — 409 p.This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models.
The book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering.Zhang Neural Networks for Online Solution of Time-Varying Linear Inequalities
Bayesian Regularized Neural Networks for Small n Big p Data
Generalized Regression Neural Networks with Application in Neutron Spectrometry
A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects
Direct Signal Detection Without Data‐Aided: A MIMO Functional Network Approach
Artificial Neural Network as a FPGA Trigger for a Detection of Neutrino-Induced Air Showers
From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy
Neural Networks for Gas Turbine Diagnosis
Application of Neural Networks (NNs) for Fabric Defect Classification
Thunderstorm Predictions Using Artificial Neural Networks
Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks
Neural Networks Applications for the Remote Sensing of Hydrological Parameters
Advanced Methods in Neural Networks-Based Sensitivity Analysis with their Applications in Civil Engineering
Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System
Neural Network Inverse Modeling for Optimization