Искусственные нейронные сети как аппроксимирующий аппарат в задачах проектирования радиотехнических устройств

В роботі розглянуто переваги застосування НМ в якості апроксимаційного апарата в задачах проектування частотовибіркових НВЧ пристроїв. Визначено та проаналізовано час та похибку навчання НМ різної конфігурації (кількість шарів та нейронів у кожному шарі) при апроксимації s-параметрів МДФ з різною кількістю резонаторів. Досліджено динаміку зміни похибки при використанні різних методів навчання НМ та проведено вибір оптимального методу навчання з врахуванням затраченого часу та похибки.Purpose. Present work is dedicated to the optimal configuration selection and training method of neural network (NN). This NN is architecture's element of modified NN ensemble accepted by authors for implementation of frequency-selective microwave devices design algorithms.Optimal configuration determining of NN. Optimal configuration determining of NN was received by analyzing the results of test NN training with different number of layers and neu-rons in these layers. The main parameters optimal configuration determining of NN is the ap-proximation quality and total learning time. Choosing of optimal teaching method. NN training methods comparison was carried out for 7 popular training methods: Levenberg-Marquardt backpropagation, BFGS quasi-Newton backpropagation, Bayesian regulation backpropagation, Conjugate gradient back-propagation with Powell-Beale restarts, Gradient descent backpropagation, Gradient descent with momentum backpropagation and Resilient backpropagation. Conclusions. NN using allows to approximate complex features of microwave devices, such as frequency dependencies of S – parameters etc. The approximation accuracy depends on configuration and method of NN training. Increasing the number of NN layers leads to im-provement of approximate characteristics. According to our results the most effective is usage of 4 layers and the number of neurons in each layer should be over the range 10 to 20. Optimal training method for complex characteristics is Bayesian regulation backpropagation, for time training reduction can be used Levenberg-Marquardt backpropagation.В работе рассмотрены преимущества применения нейронных сетей (НС) в качестве аппроксимирующего аппарата в задачах проектирования частотоизбирательных СВЧ устройств. Определено и проанализировано время и погрешность обучения НС разной конфигурации (количество слоев и нейронов в каждом слое) при аппроксимации s-параметров МДФ с разным количеством резонаторов. Исследовано динамику изменения погрешности во время использовании разных методов обучения НС и проведен выбор оптимального метода обучения с учетом затраченного времени и погрешности

Identification of Nonlinear Systems Using Radial Basis Function Neural Network

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function

Fuzzy local linear approximation-based sequential design

When approximating complex high-fidelity black box simulators with surrogate models, the experimental design is often created sequentially. LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions. The LOLA algorithm estimates gradients to identify interesting regions, but has a bad complexity which results in long computation time when simulators are high-dimensional. In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic. Experiments show the new method is a lot faster and results in experimental designs of comparable quality

Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids

Measuring efficiency with neural networks. An application to the public sector

In this note we propose the artificial neural networks for measuring efficiency as a complementary tool to the common techniques of the efficiency literature. In the application to the public sector we find that the neural network allows to conclude more robust results to rank decision-making units.DEA