Radial Basis Function Network Research Articles

Research on Coal and Gas Outburst Prediction and Sensitivity Analysis Based on an Interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine Model

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations.

Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets

ABSTRACT This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorithms for these rockets are presented. Initially, the methodology is evaluated for a robust controller based on a reference model with ANN-RBF for altitude control. The main objective of the control is to suppress the effect of non-linear uncertainties inherent in the process. The method involves mathematical and computational modelling, together with the design of adaptive controllers for stability and performance analysis. The controllers considered include model reference adaptive control (MRAC) techniques and a model reference neuro-adaptive control (MRNAC) approach. The analysis, carried out using computer simulations, evaluates the behavior of each controller in relation to system stability and performance. The final objective is to select the most suitable controller for the suborbital rocket, taking into account the system constraints, robust performance requirements, robust stability, and optimal adaptability. This research promotes the development of adaptive controllers for suborbital rockets, with possible applications in scientific research and commercial launches.

Optimizing Weather Forecasting Accuracy via Radial Basis Function Networks, Convolutional Neural Networks and Convolutional Neural Networks

Weather forecasting is crucial for various sectors, including agriculture, disaster management, and infrastructure planning. However, traditional prediction models such as Numerical Weather Prediction (NWP) and ARIMA often struggle with long-term accuracy due to the nonlinear and chaotic nature of atmospheric phenomena. The primary objective of this study was to develop and compare the performance of Radial Basis Function Networks (RBFNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs) in predicting key weather variables such as temperature, humidity, sea-level pressure, windspeed, and rainfall. Historical meteorological data from the Kenya Meteorological Department, spanning a decade, was used to train and evaluate the models. The methodology employed included data preprocessing, model training with a 70-15-15 split for training, validation, and testing, and performance evaluation using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and training time as key metrics. The results revealed that RBFNs consistently outperformed CNNs and LSTMs, particularly for stable variables like temperature and sea-level pressure, with lower RMSE and faster training times. CNNs and LSTMs, while better at capturing complex temporal patterns, struggled with chaotic variables such as rainfall and windspeed, exhibiting higher error rates and longer training times. In conclusion, RBFNs were found to be the most efficient and accurate model for real-time weather forecasting in resource-constrained environments, whereas CNNs and LSTMs may require additional tuning or hybrid approaches to improve their forecasting of more dynamic weather variables.

USING NEURAL NETWORKS FOR LANDMINE DETECTION

The issue of large mined areas and their expansion is critical due to their impact on safety and the economy. This article focuses on enhancing the efficiency of optical methods for detecting surface-laid landmines in humanitarian demining tasks. It demonstrates that one of the most promising optical methods for mine detection is hyperspectral imaging, which provides exceptionally precise analysis of the spectral composition of the radiation in the background-target environment. However, practical application of hyperspectral methods is significantly complicated by the vast size of mined territories. Therefore, mine detection must be automated. To address this challenge, the potential of artificial neural networks was investigated. The study proposes the integration of artificial intelligence, specifically RBF (Radial Basis Function) networks, to improve the efficiency and accuracy of object detection. The work compares traditional classification methods with innovative machine learning approaches. Algorithms were tested on both simulated and real data, enabling an evaluation of their ability to identify objects under varying spectral content conditions. The specific characteristics of the study area define the requirements for assessing the performance of technical tools: foremost, the probability of missing a signal must be minimized. At the same time, given the large-scale computations involved, the probability of false alarms should remain low. The study shows that RBF neural networks can detect mines with a low rate of false alarms. During network training with a large spread parameter, the sensitivity of the output to the spectral variability of pixels decreases. This allows the network to detect a target even at low fill coefficients. Thus, the research results indicate that the proposed methods significantly reduce false alarms and ensure high performance under real-world conditions.

Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models.

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 systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter's potential as a robust post-processing tool for environmental simulations.

Internet of Medical Things-enabled brain tumor classification using GWO-based optimized RBF network

Internet of Medical Things-enabled brain tumor classification using GWO-based optimized RBF network

Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions

The kernel method is a tool that converts data to a kernel space where operation can be performed. When converted to a high-dimensional feature space by using kernel functions, the data samples are more likely to be linearly separable. Traditional machine learning methods can be extended to the kernel space, such as the radial basis function (RBF) network. As a kernel-based method, support vector machine (SVM) is one of the most popular nonparametric classification methods, and is optimal in terms of computational learning theory. Based on statistical learning theory and the maximum margin principle, SVM attempts to determine an optimal hyperplane by addressing a quadratic programming (QP) problem. Using Vapnik–Chervonenkis dimension theory, SVM maximizes generalization performance by finding the widest classification margin within the feature space. In this paper, kernel machines and SVMs are systematically introduced. We first describe how to turn classical methods into kernel machines, and then give a literature review of exciting kernel machines.We then introduce the SVM model, its principles, and various SVM training methods for classification, clustering, and regression. Related topics, including optimizing model architecture, are also discussed. We conclude by outlining future directions for kernel machines and SVMs. This article functions both as a state-of-the-art survey and a tutorial.

RbfCon: Construct Radial Basis Function Neural Networks with Grammatical Evolution

Radial basis function networks are considered a machine learning tool that can be applied on a wide series of classification and regression problems proposed in various research topics of the modern world. However, in many cases, the initial training method used to fit the parameters of these models can produce poor results either due to unstable numerical operations or its inability to effectively locate the lowest value of the error function. The current work proposed a novel method that constructs the architecture of this model and estimates the values for each parameter of the model with the incorporation of Grammatical Evolution. The proposed method was coded in ANSI C++, and the produced software was tested for its effectiveness on a wide series of datasets. The experimental results certified the adequacy of the new method to solve difficult problems, and in the vast majority of cases, the error in the classification or approximation of functions was significantly lower than the case where the original training method was applied.

Basic Belief Assignment Determination Based on Radial Basis Function Network

In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.

An Enhanced Preprocessed Techniques Based 3-Level Mean Based Enhanced Histogram Equalization with A Self-Attention Segnet Based Segmentation and Fuzzy Aware Classification

Purpose: The accurate detection and classification of bone fractures in medical imaging remain significant challenges in the field of healthcare. Existing techniques often fall short of effectively identifying bone fractures due to suboptimal preprocessing methods. Moreover, the purpose of this study is to enhance the detection and classification accuracy.Methods: We introduce a novel approach incorporating a Three Level Multi-Model (TLMM)based histogram equalization to improve the accuracy. This approach encompasses Normalized Gamma-Corrected Contrast-Limited Adaptive Histogram Equalization (NGC-CLAHE) with Tanzanian Devil Optimization (TDO), CLAHE with Self-Adaptive-TDO (SA-TDO), and Adaptive Weighted Brightness Preserving Dynamic Fuzzy Histogram Equalization (AW-BPDFHE) with Giant-Devil Optimization (GDO), derived from the Giant Trevally Optimizer (GTO) and Tanzanian Devil Optimization (TDO) algorithms. Once Pre-processing is complete, the refined dataset is subjected to segmentation. For the segmentation, the SegNet model is used. Then the Gray-Level Co-occurrence Matrix (GLCM) model is used for extracting the optimal features. The obtained features are then directed to the classification layer, where a Fuzzy Radial Basis Function network (FRBF) efficiently classifies the bone fracture regions. Result: The performance of the proposed design can be validated by calculating different metrics. Moreover, the performance of the proposed model is compared with the existing models such as CNN, SVM, DT and RN. Conclusion: This integrated approach demonstrates promising results in improving the identification and classification of bone fractures in medical images, offering potential benefits to the field of medical diagnosis and patient care. On analyzing the performance of the proposed design as well as the existing model, the proposed model provides better detection and classification accuracy.

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the Radial Basis Function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

A neuromorphic radial-basis-function net using magnetic bits for time series prediction

Magnetic tunnel junctions (MTJs) are considered strong candidates for constructing neuromorphic systems owing to their low power consumption and high integrability. However, research on MTJ-based local approximation network is still lacking. In this work, we propose an MTJ-based radial basis function (RBF) network and numerically investigate its time-series prediction capability. The results demonstrate that the MTJ-based RBF network can enhance its prediction performance by utilizing increased environmental temperatures, achieving performance better than traditional software artificial neural networks.

Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss

Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.

Implementation of high step up converter using RBFN MPPT controller for fuel cell based electric vehicle application

Fuel cell-based electric vehicles (EVs) are gaining popularity in the automotive industry due to strict carbon emissions and fuel efficiency regulations. Fuel cells have inherently low voltage characteristics, making it challenging to interface with EV drive systems. This work proposes a unique topology implementing a non-isolated high step-up DC-DC converter to integrate the Proton Exchange Membrane Fuel (PEMF) cell with the EV motor drive. The converter combines a modified Single-Ended Primary Inductor Converter (m-SEPIC) with two winding-coupled inductors, enabling high voltage gain, minimizing conduction losses, and ensuring high efficiency. The output power of a fuel cell is highly dependent on cell temperature and membrane water content, making a maximum power point tracking controller essential for extracting optimal power from the fuel cell stack. The optimum power point of the PEMF cell is identified using a Neural Network (NN)-based Maximum Power Point Tracking (MPPT) technique, employing the Radial Basis Function Network (RBFN) approach compared to the Perturb & Observe (P&O) method. The performance of the RBFN MPPT method is validated under various temperature conditions of the fuel cell system. An extensive analysis and comparison with alternative converters validate the efficacy of the proposed system. The converter is designed for an input voltage of 24 V and boosts it to an output voltage of 240 V, with high static voltage gain, high power density, and minimized total losses. The system is tested and validated through both simulation and real-time implementation using the dSPACE 1104 real-time controller. A 200 W, 3000 rpm, 240 V VSI-based BLDC motor drive integrated with the proposed converter topology is developed as a real-time setup in the laboratory which would confirm the converter’s functionality. Both theoretical analysis and real-time outcomes affirm the suitability of the proposed converter topology for electric vehicle applications.

Tracking control strategy of tendon driven robotic arm under adaptive neural network

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovered every year. Therefore, early warning of infectious diseases will greatly reduce the socio-economic harm of infectious diseases. This study is based on the data of COVID-19 epidemic in China (except Macau and Taiwan Province) from 2020 to 2022. Firstly, we used ArcGIS software to analyze the spatial agglomeration pattern of the number of patients in various regions of China through global spatial autocorrelation analysis, local spatial autocorrelation analysis, center of gravity trajectory migration algorithm and other statistical tools; In addition, the areas with serious COVID-19 epidemic and requiring special attention were screened out. Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. Finally, a multi algorithm fusion learning model based on stacking technology is proposed to address the problem of poor generalization ability of single algorithm models in prediction; Furthermore, radial basis function network (RBF) was used as a two-level meta learner to fuse the above models, and particle swarm optimization (PSO) was used to optimize RBF parameters to reduce generalization error. The experimental results show that the performance of the integrated model is better than that of the single model in the COVID-19 dataset. In order to better apply the stacking model to the prediction of new infectious diseases, we applied the prediction model based on the COVID-19 dataset to the prediction of the number of AIDS and pulmonary tuberculosis (PTB) cases, and verified the wide applicability of our model in the prediction of infectious diseases.

Objects Diversity and its Impact on Classification Quality in Dispersed Data Environments

This paper studies a classification model dedicated to dispersed data, employing the k-nearest neighbors method as a base classifier and a Radial Basis Function (RBF) network as a fusion method. Focusing on classifying objects described by different attributes, the study systematically reduces common objects in local tables to assess the robustness of the model. Surprisingly, the proposed approach shows resilience in reducing common objects without significantly affecting key metrics such as F-measure, balanced accuracy and overall accuracy. Moreover, the studied model performs better on balanced data. This research contributes valuable insights into dispersed data classification, demonstrating the model’s effectiveness in handling diverse objects and attributes. The findings have implications for fields reliant on dispersed data storage, such as healthcare, banking, and surveillance, showcasing the model’s potential for real-world applications.

Introducing an integrated self-organizing map and radial basis function network for accurate prediction of water- Fe3O4 nanofluid viscosity versus solid volume fraction and temperature

Nanofluids, colloidal suspensions of nanoparticles in base fluids, promise improved thermal conductivity and energy efficiency. Predicting nanofluid viscosity, influenced by factors like temperature and nanoparticle concentration, is crucial for optimizing performance. This research introduces an innovative approach combining Self-Organizing Map (SOM) and Radial Basis Function (RBF) networks to accurately predict viscosity. The SOM guides RBF center placement for enhanced accuracy, validated with water-Fe3O4 nanofluid data. Temperature significantly affects viscosity, with adjustments from 10 to 50 °C showing viscosity decreases from around 5 to lower than 2 mPa.s. The integrated SOM-RBF model achieves high precision (max absolute error 0.1078 mPa.s, 4 % relative error), suggesting potential for further enhancement. This novel neural network combination enhances efficiency in nanofluid applications, advancing predictive modeling in nanofluid engineering for broader industrial and scientific innovation.

Measurement of body size parameters and body weight prediction in beef cattle based on image analysis

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing.