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Abstract 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 pattern recognition, the book...

This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could ...

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques fo...

Abstract Partial discharge (PD) monitoring is widely used in rotating machines to evaluate the condition of stator winding insulation, but its practice on a large scale requires the development of intelligent systems tha...

A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of t...

ABSTRACT This study is aimed to make a comparison between three artificial neural networks, these networks differ from each other in architecture and the method of adaptive the weights. In this research four ANN are used...

We present a photonic mode converter based on a grating structure, modeled and inversely designed by deep neural networks. The neural network maps the physical parameters of the grating to the grating responses, i.e., co...

Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detectio...

Thermoelectric generators offer a promising solution for sustainable, low-maintenance energy harvesting in smart agriculture, especially for powering off-grid sensor nodes and microgrid systems. However, their inherently...

The technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific archite...

We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network s...

Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single ta...

Abstract The study of multi classification of data has become one of the important issues which geographical studies focus on especially those which take their data from satellites. Two ways of classification has been ta...

Spectral reconstruction, critical for understanding sample composition, is extensively applied in fields like remote sensing, geology, and medical imaging. However, existing spectral reconstruction methods require bulky ...

The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on ...

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been ...

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; howev...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater dema...

Abstract This article presents the analysis of a hybrid, error correction-based, neural network model to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation model...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

Abstract Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis, while the noise mixed in measured signals harms the prediction accuracy of networks. Existing denoising ...

The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially wi...

Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence. The implementation of optical convolutions plays a vital...

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency...