The article is devoted to approximate methods for solving differential equations. An approach based on neural networks with radial basis functions is presented. Neural network training algorithms adapted to radial basis function networks are proposed...
The determination of The Radial Basis Function Network centers is an open problem. This work determines the cluster centers by a proposed gradient algorithm, using the information forces acting on each data point. These centers are applied to a Radia...
Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate condition...
This paper proposes a parallel fixed point radial basis function (RBF) artificial neural network (ANN), implemented in a field programmable gate array (FPGA) trained online with a least mean square (LMS) algorithm. The processing time and occupied ar...
Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered fro...
Wood–plastic composite (WPC) is being increasingly adopted in construction and furniture applications due to its durability and recyclability. This study investigates face-milling responses—resultant cutting force and cutting temperature&...
This article presents a hybrid backstepping consisting of two robust controllers utilizing the approximation property of a radial basis function neural network (RBFNN) for a quadrotor with time-varying uncertainties. The quadrotor dynamic system is d...
Fractional differential equations (FDEs) arising in engineering and other sciences describe nature sufficiently in terms of symmetry properties. This paper proposes a numerical technique to approximate ordinary fractional initial value problems by ap...
To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using...
Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the rec...
The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire l...
In this paper, we investigated a three-dimensional chaotic system that models key aspects of a monetary system, including interest rates, investment demand, and price levels. The proposed system is described by a set of autonomous quadratic ordinary...
A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is f...
Abnormal intra-QRS potentials (AIQPs) are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having th...
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for au...
Newton’s second law has been applied to create a dynamic model of the lateral motion of a supercavitating vehicle, assuming a stable cavity. However, some states cannot be measured, and there is uncertainty in the lateral model. Aiming to resol...
This paper presents a Radial Basis Function Neural Network (RBFNN) that approximates the vector field of a four-dimensional prey–migration–predator–superpredator ecological model with prey refuge, considered in deterministic and sto...
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. T...
The calorific value of coal is a key parameter for pricing, trade, and combustion management. Conventional bomb calorimetry provides accurate results but is time-consuming, labor-intensive, and destructive. Near-infrared (NIR) spectroscopy offers a r...
The rapid evolution of botnet attacks poses a critical challenge facing cybersecurity, necessitating the development of intrusion detection models that are both highly accurate and computationally efficient. This paper proposes a heterogeneous radial...
This study evaluates radial basis function neural networks (RBFNNs) for severity classification within predefined semiconductor-detector fault categories. The datasets contain simulated PN aging, RC circuit faults and radiation damage, together with...
Meta-model sre generally applied to approximate multi-objective optimization, reliability analysis, reliability based design optimization, etc., not only in order to improve the efficiencies of numerical calculation and convergence, but also to facil...
Product supply chain systems are structurally complex infophysical systems that contain numerous unmodeled dynamics and uncertainties. Drastic fluctuations in user demand and sudden unexpected events—such as epidemics, trade wars, or cyber-atta...
The thermal error modeling technology of computer numerical control (CNC) machine tools is the core of thermal error compensation, and the machining accuracy of CNC machine tools can be improved effectively by the high-precision prediction model of t...
Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is r...
1 December 2023
Visual Reinforcement Learning (RL) has been largely investigated in recent decades. Existing approaches are often composed of multiple networks requiring massive computational power to solve partially observable tasks from high-dimensional data such...
The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model ma...
Familiar and unfamiliar face recognition is an important cognitive process with potential applications in brain-computer interface (BCI) systems and neurocognitive assessment. Classification of familiar and unfamiliar faces based on electroencephalog...
A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effect...
In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algori...
The long-run relationship between economic growth and environmental quality has been estimated within the framework of the environmental Kuznets Curve (EKC). Several studies have estimated this relationship by using statistical models such as panel r...
Quadrotor unmanned aerial vehicles (UAVs) operating in complex and dynamic environments, especially when subjected to unknown disturbances such as wind, can experience significant degradation in the stability of trajectory tracking control. Current r...
Learning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-lay...
Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to e...
The combined use of the Radial Basis Function Network (RBFN) model with pretreated seawater by biomineralization (BSw) was investigated as an approach to improve copper tailings flocculation for mining purposes. The RBFN was used to set the optimal r...
Suspension systems are critical parts of modern cars. In this study, a radial basis function neural networks-based adaptive PID optimal method is presented for vehicle suspension systems. To avoid the shortcoming that the parameters of PID control ar...
A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other stan...
In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that...
Botnet architectures are evolving rapidly, creating significant threats to global network security. This paper presents a homogeneous Radial Basis Function Neural Network (RBFNN) approach for botnet detection that employs a single, uniform RBFNN arch...
Conventional methods for solving inverse wave problems struggle with ill-posedness, significant computational demands, and discretization errors. In this study, we propose an innovative framework for solving inverse problems in wave equations by usin...
The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on sy...
In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysic...
A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N,...
We develop a radial basis function neural network (RBFNN) for dynamic equations on time scales using a Gaussian-type activation generated by the time-scale exponential. The activation
This study introduces a novel enhancement to the Kalman filter algorithm by integrating it with Radial Basis Function neural networks to improve numerical weather prediction models. Traditional Kalman filters frequently underperform when used by dyna...
Detecting denial of service traffic remains challenging when malicious sessions exhibit flow characteristics that closely resemble benign network behavior, particularly in low-rate attack settings. This study examines whether autoencoder-based featur...
Forecasting crop chemical characteristics based on soil properties is not only a possible way to spare supplementary sampling and testing, but also a potential method of instructing cultivation planning based on regional soil surveys. In this paper,...
Radial basis function (RBF) networks are an established parametric machine learning tool which has been extensively utilized in data classification and data fitting problems. These specific machine learning tools have been applied in various scientif...
In this study, a new hybrid method based on the generalized finite difference method (GFDM) and radial basis function (RBF) neural network technologies is developed to solve the inverse problems of surface anomalous diffusion. Specifically, the GFDM...
The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integrati...
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