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Parameter tuning for dynamic digital twins in inverter-dominated distribution grid

IET RENEWABLE POWER GENERATION 2020年第5期14卷 811-821页

作者： Song, Xinya Jiang, Teng Schlegel, Steffen Westermann, Dirk Technol Univ Ilmenau Dept Power Syst Gustav Kirchhoff Str 1 Ilmenau Germany

This research proposed the methods based on the neural network (NN) to build the digital twins (DT) of the inverter model. The proposed methods can be divided into two groups: firstly, an online tuner for the proportional-integral (PI) controller is formulated through backpropagation (BP) algorithm;and secondly an NN-based identifier is used to approximate the nonlinear functional dynamics of the targeted control loop of the inverter. The design of PI tuner is based on the deviation between the output of the model and the reference output from measurement data. Then, according to the difference, the tuner can calculate the appropriate parameter of the current and voltage controller to track the dynamic behaviour of the reference model. The NN identifier is, however, to replicate the dynamic character of the reference model by NN which is initially trained offline with extensive test data and afterwards is applied to online tuning. In order to compare the advantages of the methods by NN with the traditional ones, the system identification and parameter estimation are also utilised to build the DT of inverter model. The performance of these methods will contribute to illustrating that NN identifier is effective in the building of DT.

关键词： PI control online tuner inverter model inverter-dominated distribution grid current controller dynamic behaviour PI tuner system identification backpropagation neurocontrollers control system synthesis adaptive control nonlinear control systems parameter estimation neural network backpropagation algorithm nonlinear functional dynamics power distribution control reference output appropriate parameter NN-based identifier dynamic digital twins invertors voltage controller parameter tuning measurement data proportional–integral controller tuning targeted control loop

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Aquifer parameters determination for large diameter wells using neural network approach

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JOURNAL OF HYDROLOGY 2002年第1-4期265卷 118-128页

作者： Balkhair, KS King Abdulaziz Univ Fac Meteorol Environm & Arid Land Agr Dept Hydrol Jeddah 21589 Saudi Arabia

Use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of groundwater hydrology and water resources problems. In this research, an ANN was developed and used to estimate aquifer parameter values, namely transmissivity and storage coefficient, from pumping test data for a large diameter well. The ANN was trained to map time-drawdown and well diameter data (input vector) to its corresponding transmissivity and storage coefficient values (output vector). Based upon a pre-specified range of aquifer parameters, the input vectors were generated from the analytical solution of Papadopulos and Copper for large diameter well in a homogeneous, isotropic, non-leaky confined aquifer. The ANN was trained with a fixed number of drawdown data points corresponding to a varying pre-specified range of aquifer parameters and time-series values. Once the network is trained to an acceptable level of accuracy, it produces an output of aquifer parameter values for any input vector. The results obtained with the ANN are in good agreement with published values. A significant advantage of the ANN approach is that it overcomes the problem of determining the storage coefficient, which when determined by traditional type curve matching method is of questionable reliability. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： aquifer parameters artificial neural network large diameter well backpropagation algorithm well hydraulics training sets

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Predicting the performance of a desulfurizing bio-filter using an artificial neural network (ANN) model

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ENVIRONMENTAL ENGINEERING RESEARCH 2021年第6期26卷 67-76页

作者： Salehi, Reza Lestari, Retno Ambarwati Sigit 3495 St Domin Montreal PQ H2X 2X5 Canada 17 Agustus 1945 Univ Chem Engn Dept Semarang Jawa Tengah Indonesia

The aim of this study was to develop a model for predicting the performance of a desulfurizing bio-filter (BF), without requiring prior information about H2S biodegradation kinetics and mechanism. A single hidden layer artificial neural network (ANN) model was developed and validated using the gradient descent backpropagation (GDBP) learning algorithm coupled with a learning rate and a momentum factor. The ANN model inputs were gas flow rate, residence time, and axial position in the BF bed. The removal efficiency of H2S was the model output. Various structures for ANN model, differing in the number of hidden layer neurons, were trained and an early stopping validation technique, the K-fold cross-validation, was used to determine the optimal structure with the best generalization ability. The modeling results showed that there was a good agreement between the experimental data and the predicted values, with a determination coefficient (R2) of 94%. This implies that the ANN model might be an attractive and useful alternative tool for forecasting the performance of desulfurizing BFs.

关键词： Artificial neural network backpropagation algorithm Bio-filter Cross-validation Hydrogen sulfide removal

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Fast training of multilayer perceptrons

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第6期8卷 1314-1320页

作者： Verma, B School of Information Technology Faculty of Information and Communication Technology Griffith University Gold Coast QLD Australia

Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain, This paper describes a new approach which is much faster and certain than error backpropagation, The proposed approach is based on combined iterative and direct solution methods, In this approach, we use an inverse transformation for linearization of nonlinear output activation functions, direct solution matrix methods for training the weights of the output layer;and gradient descent, the delta rule, and other proposed techniques for training the weights of the hidden layers, The approach has been implemented and tested on many problems, Experimental results, including training times and recognition accuracy, are given, Generally, the approach achieves accuracy as good as or better than perceptrons trained using error backpropagation, and the training process is much faster than the error backpropagation algorithm and also avoids local minima and paralysis.

关键词： artificial neural networks backpropagation algorithm local minima multilayer perceptrons supervised learning

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The solutions of vibration control problems using artificial neural networks

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2003年第5期340卷 307-325页

作者： Alli, H Uçar, A Demir, Y Firat Univ Fac Engn Dept Mech Engn Elazig Turkey Firat Univ Fac Engn Dept Elect & Elect Engn Elazig Turkey

This paper introduces an alternative method artificial neural networks (ANN) used to obtain numerical solutions of mathematical models of dynamic systems, represented by ordinary differential equations (ODEs) and partial differential equations (PDEs). The proposed trial solution of differential equations (DEs) consists of two parts: The initial and boundary conditions (BCs) should be satisfied by the first part. However, the second part is not affected from initial and BCs, but it only tries to satisfy DE. This part involves a feedforward ANN containing adjustable parameters (weight and bias). The proposed solution satisfying boundary and initial condition uses a feedforward ANN with one hidden layer varying the neuron number in the hidden layer according to complexity of the considered problem. The ANN having appropriate architecture has been trained with backpropagation algorithm using an adaptive learning rate to satisfy DE. Moreover, we have, first, developed the general formula for the numerical solutions of nth-order initial-value problems by using ANN. For numerical applications, the ODEs that are the mathematical models of linear and nonlinear mass-damper-spring systems and the second- and fourth-order PDEs that are the mathematical models of the control of longitudinal vibrations of rods and lateral vibrations of beams have been considered. Finally, the responses of the controlled and non-controlled systems have been obtained. The obtained results have been graphically presented and some conclusion remarks are given. (C) 2003 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.

关键词： vibration control problems artificial neural networks backpropagation algorithm

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A dynamic ensemble learning algorithm for neural networks

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NEURAL COMPUTING & APPLICATIONS 2020年第12期32卷 8675-8690页

作者： Alam, Kazi Md Rokibul Siddique, Nazmul Adeli, Hojjat Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh Ulster Univ Sch Comp Engn & Intelligent Syst Derry BT48 7JL Londonderry North Ireland Ohio State Univ Dept Neurosci Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive-pruning strategy, and different training samples for individual NN's learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble;(2) maintaining accuracy and diversity of NNs at the same time;and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.

关键词： Neural network ensemble backpropagation algorithm Negative correlation learning Constructive algorithms Pruning algorithms

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Epilepsy diagnosis using artificial neural network learned by PSO

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2015年第2期23卷 421-432页

作者： Yalcin, Nesibe Tezel, Gulay Karakuzu, Cihan Bilecik Seyh Edebali Univ Dept Comp Engn Bilecik Turkey Sakarya Univ Inst Nat Sci Sakarya Turkey Selcuk Univ Dept Comp Engn Konya Turkey

Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.

关键词： Artificial neural networks backpropagation algorithm electroencephalogram epilepsy diagnosis particle swarm optimization

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Real-time surface discrimination using an analog neural network implemented in a phase-shift laser rangefinder

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IEEE SENSORS JOURNAL 2007年第9-10期7卷 1381-1387页

作者： Gatet, Laurent Tap-Beteille, Helene Lescure, Marc ENSEEIHT LOSE F-31071 Toulouse France

An analog neural network (NN) was developed for real-time surface recognition by using two photoelectrical signals issued from a phase-shift rangefinder. The NN architecture consists of a multilayer perceptron (MLP) with two inputs, three neurons in the hidden layer, and one output. The NN output is compared with threshold voltages in order to classify the tested surfaces. In this type of application, analog NN implementation has many advantages, especially the small silicon area used, a low-power consumption, and no analog-to-digital conversions. This recognition system has been successfully tested for four types of surfaces (a plastic surface, a glossy paper, a painted wall, and a porous surface), at a remote distance between the rangefinder and the target varying from 0.5 in up to 1.25 in and with a laser beam incidence angle varying between -pi/6 and pi/6. This paper presents the NN training and the experimental tests of surface discrimination.

关键词： analog neural network (NN) backpropagation algorithm laser rangetinder multilayer perceptron (MLP) surface detection

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A novel weight pruning method for MLP classifiers based on the MAXCORE principle

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NEURAL COMPUTING & APPLICATIONS 2013年第1期22卷 71-84页

作者： Medeiros, Claudio M. S. Barreto, Guilherme A. Fed Inst Ceara Dept Ind BR-60040531 Fortaleza Ceara Brazil Univ Fed Ceara Dept Teleinformat Engn Ctr Technol BR-60455970 Fortaleza Ceara Brazil

We introduce a novel weight pruning methodology for MLP classifiers that can be used for model and/or feature selection purposes. The main concept underlying the proposed method is the MAXCORE principle, which is based on the observation that relevant synaptic weights tend to generate higher correlations between error signals associated with the neurons of a given layer and the error signals propagated back to the previous layer. Nonrelevant (i.e. prunable) weights tend to generate smaller correlations. Using the MAXCORE as a guiding principle, we perform a cross-correlation analysis of the error signals at successive layers. Weights for which the cross-correlations are smaller than a user-defined error tolerance are gradually discarded. Computer simulations using synthetic and real-world data sets show that the proposed method performs consistently better than standard pruning techniques, with much lower computational costs.

关键词： MLP classifier backpropagation algorithm Weight pruning Feature selection

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Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2020年第2期12卷 125-138页

作者： Rehman, Abdur Athar, Atifa Khan, Muhammad Adnan Abbas, Sagheer Fatima, Areej Atta-ur-Rahman Saeed, Anwaar Natl Coll Business Adm & Econ Dept Comp Sci Lahore Pakistan Comsats Univ Islamabad Dept Comp Sci Lahore Campus Lahore Pakistan Lahore Garrison Univ Dept Comp Sci Lahore Pakistan Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Virtual Univ Dept Comp Sci Islamabad Pakistan

Diabetes is among the most common medical issues which people are facing nowadays. It may cause physical incapacity or even death in some cases. It has two core types, namely type I and type II. Both types are chronic and influence the functions of the human body that regulate blood sugar. In the human body, glucose is the main element that boosts cells. However, insulin is a key that enters the cells to control blood sugar. People with diabetes type I do not have the ability to produce insulin. Whereas people with diabetes type II lack the ability to react to insulin and frequently do not make enough insulin. For adequate analysis of such a fatal disease, techniques with a minimum error rate must be utilized. In this regard, different models of artificial neural network (ANN) have been investigated in the literature to diagnose/predict the condition with a minimum error rate, however, there is a need for improvement. To further advance the accuracy, a deep extreme learning machine (DELM) based prediction model is proposed and investigated in this research. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows significant improvement in results compared to previous investigations. It is observed that during the investigation the proposed approach has the highest accuracy rate of 92.8% with 70% of training (9500 samples) and 30% of test and validation (4500 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

关键词： DELM ANN feedforward backpropagation algorithm diabetes prediction

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