Basis Function Artificial Neural Network Research Articles

An exposition on the prediction of load on a Smart Grid

Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.

Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

Abstract This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furthermore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.

Efficient photodegradation of disulfine blue dye and Tetracycline over Robust and Green g-CN/Ag3VO4/PAN nanofibers: Experimental design, RSM, RBF-NN and ANFIS modeling

The g-CN/Ag3VO4/PAN nanofibers (NFs) is prominent from various nanophotocatalysts (NPCs) due to their higher surface to volume ratio, mechanical strength and recyclable characteristics. In this study, g-CN/Ag3VO4/PAN NFs were obtained through in situ method by immobilizing Ag3VO4 on g-CN/PAN NFs. Compared to g-CN/PAN and Ag3VO4/PAN, the g-CN/Ag3VO4/PAN NFs revealed excellent photocatalytic performance toward disulfine blue dye (DB). Also, the g-CN/Ag3VO4/PAN NFs showed highly activity over some interference of inorganic cations/anions and excellent cycling stability. Furthermore, the batch experiments designed by central composite design (CCD) were employed for remove of tetracycline (TC). Afterward, response surface methodology (RSM), Radial basis function artificial neural network (RBF ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been used for modeling of photocatalyst dose, concentration of TC and irradiation time. Due to strong influence of pH on the photocatalytic degradation, the pH was optimized as one at a time variable. For all three models, the values of the statistical parameters were calculated. The obtained results reveal that the ANFIS models is more accurate to predict the degradation of TC. Furthermore, the effective factors were optimized by employing the Desirability function (DF) and genetic algorithm (GA) approach and then used for real water samples. At this optimum condition, the removal percentage was obtained ∼97% by DF and GA approach, respectively. At the end, the LC-Mass method was employed to detect the intermediates of photodegradation of TC molecules.

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA.

Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH≅7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9mg/L, 9.2mM, 3.2, 0.6mM, and 25min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.

Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

PurposeThe purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system.Design/methodology/approachAn IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances.FindingsThe investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO.Practical implicationsTo enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law.Originality/valueA new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

Radial Basis Function Artificial Neural Network Optimized Stability Analysis in Modified Mathematical Modeled Type-III Wind Turbine System Using Bode Plot and Nyquist Plot

Stability is the major factor that should be maintained in every power system. To predict and to optimize the nonlinear parameters this research provides a control system transient analysis using Bode plot and Nyquist plot to regulate the stability in wind power generation. The rotor speed should be balanced with respect to the generation of generator power for viable wind power generation. This study introduces a sliding mode controller for controlling wind speed and preserving system stability, and it may be improved using an Artificial Neural Network based Radial Basis Function Neural Network to eliminate nonlinearities induced by changing wind speed. The tip speed ratio approach is utilized in this study to harvest the most power from wind energy. To optimize this TSR method, a PI-RBFN tuned sliding mode controller was utilized to get maximum power while minimizing active power losses. This proposed approach may be used to address nonlinearities in the pitch angle caused by changing wind speed. As a result, the resilience of the redesigned Type-III wind turbine system is investigated using MATLAB simulink in this study. The simulation results are compared to the current DFIG-based modified Type-III wind turbine method.

Suitability evaluation of potential arable land in the Mediterranean region

The existing cultivated land in the Mediterranean region faces great pressure from various sources. A suitability evaluation of potential arable land is urgent for helping adaptation measures to mitigate the impacts of climate change and human pressure on agricultural production in the Mediterranean region. We integrated 15 biophysical and socio-economic factors from GIS and remote sensing data to perform a suitability evaluation of potential arable land in the Mediterranean region using analytical hierarchy process and radial basis function artificial neural network methods. Moreover, we analyzed the gap between potential arable land and existing cultivated land and compared the evaluation results between the analytical hierarchy process and artificial neural network methods. The results show that the suitability index of potential arable land based on artificial neural network with 6 neurons has the best correlation with average yield and average harvested area. The land area with a suitability grade over medium level accounts for 62.95% of the potential arable land area, of which 45.71% is uncultivated land. Cyprus, France, Greece, Italy, Lebanon, Portugal, Spain and Turkey have great opportunities for agricultural development. Radial basis function artificial neural network outperforms analytical hierarchy process, has better verification results, and requires less input. This study provides an initial insight into the agricultural land suitability of 16 countries around the Mediterranean Sea and introduces a research idea for agricultural land suitability evaluation.

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

This article presents a high-precision modeling method to build a small signal model of GaAs pseudomorphic high electron mobility transistor (pHEMT) by using a radial basis function artificial neural network (RBF ANN). Both the RBF ANN Method 1 that uniformly distributed spread constant (SC) in the given range and Method 2 that increasingly changed SC with a chosen step have been modeled in this work. Compared with the l RBF ANN Method 1, the RBF ANN method 2 can automatically obtain the optimal ANN corresponding to the SC. The RBF ANN method 2 is developed for S-parameters model and equivalent circuit parameters (ECPs) model of HEMTs. S-parameters model establishes S-parameters versus bias, temperature and frequency, while the ECPs model establishes ECPs versus bias and temperature. To validate the capability of the RBF ANN in small signal modeling of GaAs pHEMTs, measured and modeled data of S-parameters model and ECPs model of a 4 × 75 μm gate width, 0.15 μm gate length GaAs pHEMT are compared, and very good agreement is achieved up to 50 GHz. For the S-parameters model, the average error of the method 2 is improved by about 20% at the temperature of −20°C, 25°C, and 85°C. For the ECPs model, the average error of the method 2 is 80% higher than that of the method 1.

Machine Learning Approaches to Estimate Flow Rate of Drip Irrigation Emitter Based on Structure Parameters and Pressure

The agricultural water-saving irrigation systems need to be adapted to local conditions, and irrigation emitters with different flow rates need to be designed to meet actual needs. In this study, machine learning approaches were used to predict the flow rate of the labyrinth emitters. The structural parameters of the emitter [number of channel units (N), channel unit length (L), channel width (W), tooth height (H) and channel depth (D)] and working pressure (P) were considered as independent variables. The applied machine learning models included k-nearest neighbor (KNN), multi-layer perceptron (MLP), support vector machine (SVM) and radial basis function artificial neural network (RBF-ANN). The accuracy of the machine learning model was evaluated by the mean square error (MSE) and coefficient of determination (R 2). The results show that the MSE and R2 of the RBF-ANN model were higher than the corresponding parameters of the other three models, indicating the RBF-ANN model can predict the flow rate of the labyrinth emitter more accurately. The research provides a reference for the rapid design of emitters with different flow rate. It is helpful to realize the automation of agricultural irrigation, and then realize the construction of a smart agricultural irrigation system.

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

The importance of permafrost in the steady and fast increase in net primary production of the grassland on the Qinghai–Tibet Plateau

Permafrost affects soil water and soil temperature regimes; however, its effects on net primary production (NPP) remain unknown. Here, we examined temporal-spatial changes in grassland NPP during 2000–2018 in permafrost and permafrost-free areas on the Qinghai–Tibetan Plateau using the random forest (RF) and radial basis function artificial neural network (RBF-ANN). Our results indicated that the areas that showed increasing, decreasing, and non-significant trends for NPP accounted for 13.88%, 1.90%, and 84.22% of the permafrost area, respectively. For the permafrost-free areas, these NPP trends accounted for 22.25%, 2.68%, and 75.07% of the permafrost-free area, respectively. The mean NPP in the permafrost regions showed a faster and steadier (1.520 g C/m2/yr, p < 0.05) increase than in non-permafrost regions (1.224 g C/m2/yr, p < 0.05). The Biome-BGC model confirmed that these spatial NPP patterns could be attributed to differences in soil water and soil temperature between permafrost and permafrost-free areas. Both the soil temperature and soil water content in permafrost sites exhibited relatively lower variance than in permafrost-free sites. Although many factors may be attributed to these patterns, our results suggest that there is a possibility that the relatively stable change in permafrost NPP can be explained by the fact that permafrost can regulate soil water and temperature regimes. Therefore, climate warming can increase NPP in cold regions, and permafrost degradation may destabilize the grassland ecosystem, which may cause NPP values to exhibit greater interannual changes in the future.

Analysis and optimization of louvered separator using genetic algorithm and artificial neural network

Louvered separators are widely used in many engineering applications. This study aims to optimize four geometrical parameters: the blade length (Lb), the gap between blades (Hg), and the dust container width (Ld) and height (Hd). The objective functions were the Euler number and cut-off diameter. First, a direct multi-objective optimization study was performed using the CFD results. Then, a surrogate-based approach using the radial basis function artificial neural network for single and multi-objective optimization was employed. Increasing the height of the collector and the gap distance between the blades increased the pressure drop, while increasing the blade length and the collector length decreased the pressure drop. Furthermore, increasing the height of the collector and the blade length decreased the separator efficiency, while increasing the gap between the blades and the collector length increased the efficiency. The optimized cut-off diameter was 1.663 μm, and the minimized Euler number, Eu, was 21.03.

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM &amp; P-BSHADE, RF &amp; P-BSHADE, and RBF-ANN &amp; P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF &amp; P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF &amp; P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

Chemical model-based optimization of a sensor array for simultaneous determination of glucose and fructose

In order to present the general idea of model based optimization, the chemical system of interaction between boronic acids and diols was selected to implement the idea on a real chemical system for simultaneous determination of analytes. The reaction between diols and phenylboronic acids changes pH and the variation in pH depends on the diols concentrations.Glucose and fructose were determined using a sensor array including two sensor elements with different concentrations of phenylboronic acid in the presence of NaOH. Here, the sensor array denotes the different sensor solutions in which their responses to analytes are studied. Each sensor element solution contains phenylboronic acid (as a main part of the sensor) and a pH indicator for monitoring the pH change as a consequence of analytes and phenylboronic acid interaction. The optimum compositions of sensor elements were selected using the results of the calculation based on the chemical model and known equilibrium constants. Fructose and glucose were determined using the nonlinear calibration method (radial basis function artificial neural network) in concentration range 0.5–7 mM and 1–9 mM with root mean squares error prediction (RMSEP) 0.2 and 0.8 mM, respectively. The calculated figures of merit showed that the sensor is more sensitive to fructose.

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.

A Modified Algorithm for Training and Optimize RBF Neural Networks Applied to Sensor Measurements Validation

This paper presents the use of a radial basis function artificial neural network to estimate sensor readings exploring the analytical redundancy via auto-association. However, in order to guarantee optimal performance of the network, the training and optimization processes have been modified. In the conventional training algorithm, even if a stop criterion, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering of the neural network, may not be satisfactory while validating sensor measurements. Essentially, the proposed modification in the training algorithm is based on seeking to ensure that one or more of the metrics are met. This paper describes the proposed algorithm including all of its mathematical foundation. Afterward, a data set of a water injection pump for an oil and gas processing unit was used to train the RBF network using the conventional and the modified algorithm, and the performance of each was evaluated. Furthermore, the AAKR model is applied to the same dataset as a quality reference parameter. Finally, a comparison analysis of the developed models is presented for each of the performance metrics, as well as for overall effectiveness, demonstrating that the main advantage of the proposed approach is to obtain the estimation results equivalent or superior to the AAKR with shorter runtime and the disadvantage of having higher complexity during the model training.

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.

Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA254 and Cl2) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N25 = 46–69%, rp = 0.334–0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA254 and Cl2 can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N25 and rp values for testing datasets reached 85–92% and 0.813–0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.

Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.