Nonlinear identification via connected neural networks for unsteady aerodynamic analysis

A novel nonlinear system identification approach is presented based on the coupling of a neuro-fuzzy model (NFM) with a multilayer perceptron (MLP) neural network. Therefore, the recurrent NFM is employed for multi-step ahead predictions, whereas the MLP is subsequently used to perform a nonlinear quasi-static correction of the obtained time-series output. In the present work, the proposed method is applied as a reduced-order modeling (ROM) technique to lower the effort of unsteady motion-induced computational fluid dynamics (CFD) simulations, although it could be utilized generally for any nonlinear system identification task. For demonstration purposes, the NLR 7301 airfoil is investigated at transonic flow conditions, while the pitch and plunge degrees of freedom are simultaneously excited. In addition, the sequential model training process as well as the model application is presented. It is shown that the essential aerodynamic characteristics are accurately reproduced by the novel ROM in comparison to the full-order CFD reference solution. Moreover, by examining the results of the NFM without MLP correction it is indicated that the new approach leads to an increased fidelity regarding nonlinear ROM-based simulations.

The application of reduced-order modeling (ROM) techniques in the context of aerodynamic nonlinear system identification of realistic aircraft configurations gained increasing attention in recent years. Therefore, in the present study the application of a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is introduced concerning the computation of transonic buffet aerodynamics. In particular, the intention of the ROM is the prediction of coefficient time-series trends in contrast to a precise resolution of detailed flow effects. Further, a reduction of computational time compared to a full-order reference Computational Fluid Dynamics (CFD) solution is pursued. The training of the ROM is accomplished based on a data set computed by means of unsteady Reynolds-averaged Navier-Stokes (URANS) simulations. The performance of the trained ROM is demonstrated by predicting the buffet flow characteristics of the NASA Common Research Model (CRM) investigated at transonic flow conditions. Therefore, the wing of the configuration is excited by an external pitching motion beyond buffet onset. By comparing the ROM result with a reference URANS solution, a precise prediction capability of the aerodynamic characteristics as well as a reduction in computational time is demonstrated.KeywordsBuffet aerodynamicsReduced-order modelingMultilayer perceptron neural networkNeuro-fuzzy modelsNonlinear system identification

Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca2+), magnesium ions (Mg2+), sodium ions (Na+), chloride ions (Cl−), sulfate ( SO 42−), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R2) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.

With the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simultaneously. To tackle this challenge, this research focuses on the low accuracy issues of the traditional Blade Element Momentum theory (BEM) in predicting the aerodynamic performance of wind turbine blades. Consequently, a correction framework is proposed, to integrate the Computational Fluid Dynamics (CFD) method with the Multilayer Perceptron (MLP) neural network. In this approach, the CFD method is used to predict the airflow characteristics around the blades, and the MLP neural network is employed to model the intricate functional relationships between multiple influencing factors and key aerodynamic parameters. This process results in high-precision predictive functions for key aerodynamic parameters, which are then used to correct the traditional BEM. When this correction framework is applied to the rotor of the IEA 15 MW wind turbine, the effectiveness of MLP in predicting key aerodynamic parameters is demonstrated. The research findings suggest that this framework can enhance the accuracy of BEM aerodynamic load predictions to a level comparable to that of RANS.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination (<TEX>$R^2$</TEX>) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

Research into the problem of classification of radar returns from the ionosphere has been taken up as a challenging task for the neural networks (NNs). It appears from the literature review that for the Multi layer Perceptron (MLP) NN trained with backpropagation, reported average classification accuracy was about 96% on the test instances. This paper investigates and designs an optimal classifier using a radial basis function (RBF) NN. Authors compare the performance of two NN configurations, namely a well-known MLP NN model and the proposed RBF NN model on the radar dataset collected from the published studies. It is shown that the proposed RBF NN, consistently, has 100% accuracy on bad instances and 99.1935% accuracy on good instances. The results show that the proposed RBF NN classifier clearly outperforms the MLP NN one in various performance measures such as MSE, NMSE, correlation coefficients, area under the ROC curve and classification accuracy on the testing datasets even after attempting different data partitions.

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

Reduced-order modeling using the Volterra series approach has been successfully applied in the past decades to weakly nonlinear aerodynamic and aeroelastic systems. However, aspects regarding the identification of the kernels associated with the convolution integrals of Volterra series can profoundly affect the quality of the resulting reduced-order model (ROM). An alternative method for their identification based on artificial neural networks is evaluated in this work. This relation between the Volterra kernels and the internal parameters of a time-delay neural network is explored for the application in the reduced-order modeling of nonlinear unsteady aerodynamic loads. An impulse-type Volterra-based ROM is also under consideration for comparison. All aerodynamic data used for the construction of the ROMs are obtained from computational fluid dynamics (CFD) simulations of the NACA 0012 airfoil using the Euler equations. Prescribed inputs in pitch and in plunge degrees of freedom at different freestream Mach numbers are used to evaluate the range of applicability of the obtained models. For weakly nonlinear test cases, the modeling performance of the neural network Volterra ROM was comparable to the impulse-type ROM. Additional accuracy and adequate modeling of stronger nonlinearities, however, could only be attained with the inclusion of the neural network kernels of higher order in the Volterra ROM. A generic expression is derived for the kernel function of th-order from the internal parameters of a time-delay neural network.

Neural networks are a soft computing technique with wide application in signal processing as well as system and process modeling. In the present study, multilayer perceptron (MLP) neural networks were employed to process multidimensional signals generated in metal machining operations (including three-dimensional cutting force signals and three-dimensional cutting vibration signals) and to establish a model for predicting the machined surface roughness. This paper describes in detail our methods of multidimensional signal processing and modeling with MLP neural networks. The MLP neural network model developed in the present study fills an important research gap by taking into account the critical effect of tool-edge radius in machining. As compared to regression models, the MLP neural network model developed in the present study has significantly higher accuracy in predicting the machined surface roughness.

In this paper, a multi-layered perceptron (MLP) neural network (NN) is proposed to nullify the effects of the measured data errors associated with phasor measurement units (PMUs). With this MLP NN, power system voltage stability is estimated by calculating voltage collapse indices (VCIs). Several VCIs are compared with two evaluation criteria to determine those appropriate indices that should be trained using NN as the target output. In other words, the data obtained from the PMU are exploited as the MLP NN inputs, and the exact informative VCIs are considered as its output. The capability of the proposed method is demonstrated by applying it to the New England 39-bus test system. The obtained results confirm the validity of the proposed technique.

Demand forecasting is critical for the pharmaceutical industry to ensure efficient production, inventory management, and distribution, especially in dynamic and competitive markets. This study addresses the challenges of accurate demand prediction within the Tunisian pharmaceutical sector. The primary aim was to compare the forecasting accuracy of two methods, Holt-Winters (HW) and Multilayer Perceptron (MLP) neural networks, for three drug categories: Antiviral, Antibiotic, and Pain Relief. Additionally, the study provides actionable recommendations to enhance forecasting strategies. A 24-month (n=24) historical sales dataset (October 2020 to September 2022) from a Tunisian pharmaceutical demand forecasting company was analyzed. The analysis utilized the Holt-Winters model to incorporate seasonal adjustments and an MLP neural network to capture complex, non-linear sales patterns. Both models were evaluated using metrics such as mean squared error (MSE) to quantify prediction accuracy. The MLP neural network consistently and significantly outperformed the Holt-Winters method, demonstrating markedly lower MSE values (e.g., 0.0206 for Antivirals, compared to 30.06 for HW) and greater adaptability to demand variability across all drug categories. While effective for seasonal patterns, HW struggled with irregular fluctuations and complex dynamics. This study highlights the superiority of MLP neural networks for pharmaceutical demand forecasting due to their adaptability and accuracy in handling non-linear and variable data. The findings provide a strong quantitative basis for Tunisian pharmaceutical companies to adopt advanced machine learning techniques for more reliable planning. However, to fully realize the potential of these techniques and address the limitations outlined in this research, future research should explore hybrid models and integrate extended datasets that incorporate external market dynamics and trends.

Bidikmisi scholarships are tuition assistance for prospective students who are economically disadvantaged but have the good academic potential to study at State Universities in Indonesia . With the amount Bidikmisi applicants, there is a need to use a system that can speed up the selection process and avoid human errors. The purpose of this study is to develop a classification system for Bidikmisi scholarship applicants at Universities in Indonesia. This study uses a multilayer perceptron (MLP) neural network to build a classification model. Because MLP has some hidden layers, MLP has advantages to predict or analyze complex problems. The methodology in this study is data collection, data preprocessing, data encoding, data modeling, and evaluation. The results of the classification of bidikmisi scholarship applicants are divided into two classes, namely accepted and rejected. The multilayer perceptron method generates an accuracy value of 72.81%. The Multilayer perceptron is compared with other classification methods, namely Naïve Bayes and K-Nearest Neighbors (KNN). The results of the classification using the Naïve Bayes method obtained an accuracy value of 79.47%, while the accuracy value using KNN was 78.33%. Evaluation using MLP resulted in precision, recall, and F1-measure values of 68.36%, 72.81%, and 66.33%. the results of the evaluation using Naïve Bayes were 75.58%, 79.47%, and 71.24%, while the KNN values were 71.90%, 78.33%, and 72.53%. In order to the conclusion in this research is the MLP method can generate good performance for the classification of bidikmisi scholarship applicants. However, Naïve Bayes’ performance is better than MLP for the classification of bidikmisi scholarship applicants.