Radial Basis Function Neural Network Research Articles

Deep learning perspective on the construction of SPOC teaching model of music and dance in colleges and universities

In the era of knowledge economy, social development has higher requirements for artistic talents’ quality. The teaching mode of music and dance in colleges and universities needs to be changed urgently. This study introduced deep learning theory and designed a SPOC teaching mode for music and dance in universities to improve the quality of music and dance teaching. Firstly, the K-means clustering algorithm was used to extract the features of online learning behavior. Radial basis function neural network was used to predict students’ performance to improve teachers’ mastery of students’ online learning behavior. Subsequently, the teaching quality evaluation system was constructed using hierarchical analysis. Finally, the effectiveness of the teaching model was tested. The results showed that under this teaching model, students’ overall literacy score reached a maximum of 11.5, and their average score in music and dance reached a maximum of 94, an increase of 11 points compared to the SPOC teaching mode. This indicates that the teaching mode gives full play to the advantages of SPOC teaching, enhances students’ enthusiasm and initiative in learning, and improves their professionalism and comprehensive quality.

Compatible multi-model output fusion for complex systems modeling via set operation-based focus data identification

Is an inferior model completely useless in complex systems modeling? This study proposes a novel multi-model output fusion approach that makes the best of the outputs from multi-models, i.e., using the limited superior results from inferior models to compensate for certain inferior results from even superior models. For fusing the outputs from multi-models, the weight assigned to each output is calculated based on two factors, namely the accuracy of each model and the similarity between the testing input and the focus data used for constructing the respective model. Specifically for similarity calculation, the focus data list is identified based on set operations. There are three theoretical contributions of this study, namely accuracy-and-similarity-based weight calculation, the set-operation-based similarity calculation which is an addition to traditional distance-based calculation, and the high compatibility of the proposed approach which is independent from any baseline approach. A practical case of overall reconnaissance capability evaluation of the Unmanned Aerial Vehicle (UAV) swarm is studied for validation. Primary results indicate that the proposed approach can outperform two single models: the backpropagation neural network (BPNN) and the Radial Basis Function (RBF) neural network. Further validations demonstrate that the proposed approach also outperforms multi-model output fusion with equal weights, without model accuracy, with varied focus data percentages ranging from 0.1 to 0.9. More importantly, the proposed approach remains effective in four different conditions of multi-model outputs fusion with improvements from 9.58 % to 38.03 %.

Observer-based control for consensus tracking of non-linear synchronous generators system using sliding mode method and a radial basis function neural network

Observer-based control for consensus tracking of non-linear synchronous generators system using sliding mode method and a radial basis function neural network

Intelligent MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system. Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes.

Modeling and identification of hysteresis of marine damper considering shock environment based on evolutionary sparrow search algorithm

The mechanical properties of marine dampers are usually highly nonlinear in order to adapt to dynamic and shock environments. The dampers are subjected to dynamic and shock tests and the hysteresis curves are found to be rate-dependent and asymmetric. To be able to describe the dynamic hysteresis and shock hysteresis, a rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model is proposed based on the generalized Prandtl-Ishlinskii (GPI) model, which is a hybrid of the GPI model and the radial basis function (RBF) neural network. Due to the large number of parameters in the model, thus aiming to improve the effect of parameter identification, an evolutionary sparrow search algorithm mixed with sparrow search algorithm and differential evolutionary algorithm is proposed. Compared with other algorithms, sparrow search algorithm is verified to have ideal optimization effect and convergence speed, and it is not easy to fall into the local optimum. Finally, the RDGPI model is experimentally verified, and the results show that the prediction error of the RDGPI model for dynamic hysteresis is within 0.06 kN, and the prediction error for shock hysteresis is within 0.8 kN, which is capable of describing the rate-dependent characteristics of the damper hysteresis. And it has a very good generalization ability, which makes it very suitable to be used in the modeling of the mechanical characteristics of the marine damper.

A novel improved hybrid neural network for predicting heating load in airport building

In order to guarantee the efficient and energy-saving operation and control of airport heating systems, accurate heating load prediction is extremely critical. In view of the relatively little research on heating load prediction of airport buildings, besides, out of these, the fact that the actual characteristics of the airport itself and its production operations should be considered, resulting in inferior performance. Framed in this goal, Initially, the heating area is partitioned. Subsequently, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm synthesizes it with fuzzy entropy (FE) to decompose the heating load, and the maximum information coefficient integrated with the distance correlation coefficient is utilized for the feature selection. After that, a multi-strategy improved artificial bee colony algorithm (MIABC) algorithm is used to optimize the radial basis function (RBF)neural network and the gated recurrent unit (GRU) neural network, respectively, and the two are combined to construct the hybrid prediction model of the heating load. Finally, actual heating load data is used to verify the prediction performance of the proposed model. Besides, the results show that the root mean square error (RMSE), mean absolute percentage error (MAPE), range of relative error (RRE), and mean bias error (MBE) of the proposed model are 0.465, 1.08 %, 11.42 % and 0.082, respectively, the coefficient of determination (R2) is 0.997, and the prediction accuracy reaches 98.92 %. Additionally, it has more robustness and generalization power, effectively overcoming the heating load's uncertainties and laying a foundation for controlling, monitoring, and managing the buildings' operation.

Adaptive optimal concurrent control for detumbling space non-cooperative target via multipoint repeated contact

Space debris generally exhibits complex tumbling motions, and its direct capture may cause damage to space manipulator and base spacecraft. Current detumbling strategies typically require continuous contact collisions with the target and do not take into account actuator limitations. Thus, this paper presents an adaptive optimal concurrent control of space free-floating multi-fingered robot (SFMR) for multipoint repeated contact detumbling of non-cooperative targets. Firstly, the multipoint repeated intermittent contact model between the SFMR and the target is established. Further, an optimal admittance control that considers manipulator actuator limits and target motion bounds is formulated to generate a compliant detumbling trajectory. Through transforming the state and input inequality constraints into extended dynamical subsystems and saturation functions, respectively, the optimal control problem (OCP) is transformed into a readily solvable equality constraint problem. Moreover, an enhanced nonsingular terminal sliding mode (ENTSM) control with radial basis function neural network (RBFNN) compensation is presented in the presence of lumped uncertainties and external disturbances, which achieves rapid finite-time convergence and low-chattering. The simulation results show that the proposed method can reduce the velocity of the space target effectively without causing base spacecraft interference while achieving accurate trajectory tracking.

Dual neural network for contact temperature and cooling state monitoring in water-lubricated bearings

Harsh operating conditions subject the water-lubricated stern bearings of a ship to a non-uniform stress field, leading to frequent boundary lubrication between the bearing and shaft. Poor lubrication results in local temperature rise due to friction heat, leading to heat aging or even carbonization of the bearings. This study proposes a novel universal monitoring approach based on the internal temperature of water-lubricated bearings (WLBs). Radial basis function neural networks (RBFNN) were established to evaluate the surface temperature and cooling statuses of the bearing surface during rubbing, in which simulation results obtained from a temperature model were used as a training set. The accuracy of the simulation results and prediction model was verified through experimental data. The results showed that the maximum error of predicted contact surface temperatures is only 0.84 °C, 0.02 °C, and 1.00 °C in MAE, MAPE, and RMSE, respectively. The accuracy of cooling conditions identification can be achieved at 91.7 % with 600 s of continuous temperature data. The scheme proposed in this study can effectively predict thermal failures of water-lubricated bearings without requiring extensive experimentation, which promotes the development of intelligent water-lubricated bearings.

Comparative analysis of data driven rainfall-runoff models in the Kolar river basin

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

This article addresses the control issues of underwater manipulator arms in complex marine environments, proposing a composite control strategy based on the Harris Hawk Optimization (HHO) algorithm and Radial Basis Function (RBF) neural network. Combining the global search capability of the HHO algorithm with the fast approximation characteristics of RBF neural networks, a self-adaptive control method for underwater manipulator arms is designed. By automatically optimizing the parameters of the neural network, the performance and robustness of the control system are enhanced. Through simulation experiments, the effectiveness of the proposed control algorithm is verified. The results show that compared with the traditional RBF neural network control and fuzzy sliding mode control, the optimized control algorithm proposed in this paper has significant improvement compared with both of them, demonstrates good control effect and high practical value, and provides an effective solution for the precise control of the underwater manipulator arm.

Laser-Tracing Multi-Station Measurements in a Non-Uniform-Temperature Field

Due to the increasing requirements for the improvement of the accuracy of large coordinate-measuring machines (CMMs), the laser-tracing multi-station measurement technology, as one of the advanced precision measurement technologies, is worth studying in depth in terms of its practical application for the compensation of errors in large CMMs. Since it is difficult to maintain a constant temperature of about 20 °C in the actual workshop under the influence of solar radiation and convective heat transfer, there is a gradient in the spatial temperature distribution, and the overall temperature changes with the influence of external factors with synchronous hysteresis, it is difficult for the actual calibration environment to meet the standard environmental requirements. Therefore, the influence of temperature and other environmental factors on the accuracy of laser ranging and large-scale CMM calibration should not be ignored. In this paper, on the basis of analyzing the temperature distribution and change rule of large CMM measurement space under different working conditions, the radial basis function (RBF) neural network algorithm was used to build a non-uniform-temperature field model, and based on this model and the measurement principle of the laser-tracking instrument, the method of laser tracking and interferometric ranging accuracy enhancement was put forward under a non-uniform-temperature field. Finally, based on the multi-station technique of laser tracing, an accurate solution for the volumetric error of large CMMs under the condition of non −20 °C ambient temperature was realized. Simulation results proved that compared with the traditional temperature-compensation method, the proposed method improved the measurement accuracy of the volumetric error of a large-scale CMM using laser-tracing multi-station technology in a non-uniform-temperature field by 33.5%. This study provides a new approach for improving the accuracy of laser-tracer multi-station measurement systems.

Adaptive Visual Control for Robotic Manipulators with Consideration of Rigid-Body Dynamics and Joint-Motor Dynamics

A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

RBF-based event-triggered fixed-time stable and chattering-free controller for load following of the pressurized water reactor

Preventing chattering phenomenon in load following control of the Pressurized Water Reactor (PWR) is still a main challenge. This article focuses on the chattering-free and rapid load following control strategy for PWR. Firstly, by proposing a newly adaptive updated law, the Radial Basis Function Neural Networks (RBFNNs) is employed to compensate the unmeasured states of relative delayed neutron precursor density, average fuel temperature and uncertain disturbances simultaneously online. On this basis, a fixed-time stable controller including saturation function is constructed to guarantee that the actual output power (OP) of nuclear reactor can follow the desired power (DP) within a fixed-time and the chattering phenomenon is alleviated. Additionally, to save the limited network resource and reduce the cost of computation, an event-triggered mechanism is constructed. Based on it, a chattering-free fixed-time stable controller is structured to ensure that the actual reactivity of control rod (ARCR) converges to the desired control rod reactivity (DCRR) within a fixed-time, meanwhile, it can eliminate Zeno phenomenon during the control process. Finally, the effectiveness and feasibility of the theoretical results are demonstrated through simulation examples.

Predictive algorithm of instrument health condition based on linear regression and radial basis function neural network

Abstract Enterprises and institutions engaged in social production activities need to apply for mandatory verification of general instruments and meters on production equipment according to law, and its verification period is determined by national or local verification and calibration procedures, but the health condition in the calibration intervals is difficult to know. To improve equipment reliability, this paper relies on the linear regression model to research the health condition of general instruments and meters, uses MATLAB tools to analyze the data samples of typical equipment pressure gauge, and combines radial basis function neural network to form a complete algorithm, and finally provides a complete implementation scheme for the health condition prediction of general instruments and meters in service.

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems.

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

Neural Network-Based Adaptive Tracking Control for Denitrification and Aeration Processes With Time Delays.

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural controller is presented to achieve the satisfying control performance for WWTPs. With the advantages of radial basis function neural networks (RBF NNs), the unknown dynamics in WWTPs are identified. Based on the mechanistic analysis, the time-varying delayed models of the denitrification and aeration processes are established. Based on the established delayed models, the Lyapunov-Krasovskii functional (LKF) is used to compensate for the time-varying delays caused by the push-flow and recycle flow phenomenon. The barrier Lyapunov function (BLF) is used to ensure that the dissolved oxygen (DO) and nitrate concentrations are always kept within the specified ranges though the time-varying delays and disturbances exist. Using Lyapunov theorem, the stability of the closed-loop system is proven. Finally, the proposed control method is carried out on the benchmark simulation model 1 (BSM1) to verify the effectiveness and practicability.

Prediction of saturation exponent for subsurface oil and gas reservoirs using soft computing methods

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.

Neural network-based control for the on-orbit assembly of heterogeneous spacecraft cluster based on Vicsek fractal

On-orbit assembly is a highly effective technique for building large space structures. This paper presents the dynamics and control of an on-orbit assembly of a large space structure based on a heterogenous spacecraft cluster comprising both rigid and flexible spacecraft. The topology of the large space structure is inspired by the Vicsek fractal. A distributed assembly strategy is proposed for the spacecraft team. Radial Basis Function neural networks (RBFNNs) are utilized to approximate the bounded uncertain terms in translational and attitude dynamics. To avoid collisions during the pre-assembly phase, a neural network (NN)-based controller with collision avoidance force is designed for relative position motion. During the assembly phase, only proportional-derivative (PD) law is employed for relative position control. In addition, an NN-based controller is designed for relative attitude control for both rigid and flexible spacecraft. To estimate the unmeasured flexible vibrations, modal coordinate observers are introduced for the flexible spacecraft. The stability of the closed-loop system is proved via Lyapunov functions. Moreover, numerical results are presented to validate the effectiveness of the proposed controllers and assembly strategy.

Fixed-time event-triggered adaptive control of manipulator system with input deadzone and model uncertainty

There exist universal model uncertainty and input deadzone in mechatronic motion plant due to unknown model parameters and actuator physical feature, which will degrade the motion performance and stability. In this study, a fixed-time event-triggered adaptive control is proposed in n-DOF manipulator system to address these problems. Firstly, the dynamic model of n-DOF manipulator with joint friction and input deadzone is constructed by Lagrange technique. Then a radial basis function neural network (RBFNN) is designed to approximate uncertain dynamics. Meanwhile, an event-triggered mechanism (ETM) is used to reduce the control update frequency and to save the communication resource. Furthermore, an adaptive estimation law is adopted to compensate unknown input deadzone parameters. Based on backstepping iteration technique, a fixed-time convergent controller is presented to guarantee the system state errors convergence to zero neighborhood in finite time irrelevant to arbitrary initial state. Finally, the effectiveness of the proposed control scheme is verified by sufficient comparative simulation and experimental results.