AIMC Topic: Least-Squares Analysis

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Nonfragile H State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional-Integral Observer Design.

IEEE transactions on neural networks and learning systems
In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped wit...

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

Sensors (Basel, Switzerland)
Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-a...

Short-term wind speed prediction using hybrid machine learning techniques.

Environmental science and pollution research international
Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have bee...

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

Lasers in medical science
The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum sp...

Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models.

Neural networks : the official journal of the International Neural Network Society
Reservoir computing is a machine learning framework derived from a special type of recurrent neural network. Following recent advances in physical reservoir computing, some reservoir computing devices are thought to be promising as energy-efficient m...

Capped L-norm metric based robust least squares twin support vector machine for pattern classification.

Neural networks : the official journal of the International Neural Network Society
Least squares twin support vector machine (LSTSVM) is an effective and efficient learning algorithm for pattern classification. However, the distance in LSTSVM is measured by squared L-norm metric that may magnify the influence of outliers. In this p...

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors.

Sensors (Basel, Switzerland)
Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of...

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry.

Sensors (Basel, Switzerland)
Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocess...

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...