Розробка методу навчання штучних нейронних мереж для інтелектуальних систем підтримки прийняття рішень

A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and the type and amount of information received at the input of the artificial neural network. The specified method allows the training of an individual network node and the combination of network nodes. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, with unambiguous decisions being made. This training method provides on average 10–18 % higher learning efficiency of artificial neural networks and does not accumulate errors during training. The specified method will allow training artificial neural networks, identifying effective measures to improve the functioning of artificial neural networks, increasing the efficiency of artificial neural networks through training the parameters and architecture of artificial neural networks. The method will allow reducing the use of computing resources of decision support systems, developing measures aimed at improving the efficiency of training artificial neural networks and increasing the efficiency of information processing in artificial neural networksРазработан метод обучения искусственных нейронных сетей для интеллектуальных систем поддержки принятия решений. Метод проводит обучение не только синаптических весов искусственной нейронной сети, но и вида и параметров функции принадлежности; архитектуры и параметров отдельного узла сети. В случае невозможности обеспечить заданное качество функционирования искусственных нейронных сетей за счет обучения параметров искусственной нейронной сети происходит обучение архитектуры искусственных нейронных сетей. Выбор архитектуры, вида и параметров функции принадлежности происходит с учетом вычислительных ресурсов средства и с учетом типа и количества информации, поступающей на вход искусственной нейронной сети. Указанный метод позволяет проводить обучение отдельного узла сети и осуществлять комбинирование узлов сети. Разработка предложенного метода обусловлена необходимостью проведения обучения искусственных нейронных сетей для интеллектуальных систем поддержки принятия решений, с целью обработки большего количества информации, при однозначности решений, которые принимаются. Указанный метод обучения обеспечивает в среднем на 10–18% более высокую эффективность обучения искусственных нейронных сетей и не накапливает ошибок в ходе обучения. Указанный метод позволит проводить обучение искусственных нейронных сетей; определить эффективные меры для повышения эффективности функционирования искусственных нейронных сетей; повысить эффективность функционирования искусственных нейронных сетей за счет обучения параметров и архитектуры искусственных нейронных сетей. Метод позволит уменьшить использование вычислительных ресурсов систем поддержки и принятия решений; выработать меры, направленные на повышение эффективности обучения искусственных нейронных сетей; повысить оперативность обработки информации в искусственных нейронных сетяхРозроблено метод навчання штучних нейронних мереж для інтелектуальних систем підтримки прийняття рішень. Метод проводить навчання не тільки синаптичних ваг штучної нейронної мережі, але й виду та параметрів функції належності; архітектури та параметрів окремого вузла мережі. В разі неможливості забезпечити задану якість функціонування штучних нейронних мереж за рахунок навчання параметрів штучної нейронної мережі відбувається навчання архітектури штучних нейронних мереж. Вибір архітектури, виду та параметрів функції належності відбувається з врахуванням обчислювальних ресурсів засобу та з врахуванням типу та кількості інформації, що надходить на вхід штучної нейронної мережі. Зазначений метод дозволяє проводити навчання окремого вузла мережі та здійснювати комбінування вузлів мережі. Розробка запропонованого методу обумовлена необхідністю проведення навчання штучних нейронних мереж для інтелектуальних систем підтримки прийняття рішень, з метою обробки більшої кількості інформації, при однозначності рішень, що приймаються. Зазначений метод навчання забезпечує в середньому на 10–18 % більшу високу ефективність навчання штучних нейронних мереж та не накопичує помилок в ході навчання. Зазначений метод дозволить проводити навчання штучних нейронних мереж; визначити ефективні заходи для підвищення ефективності функціонування штучних нейронних мереж; підвищити ефективність функціонування штучних нейронних мереж за рахунок навчання параметрів та архітектури штучних нейронних мереж. Метод дозволить зменшити використання обчислювальних ресурсів систем підтримки та прийняття рішень; виробити заходи, що спрямовані на підвищення ефективності навчання штучних нейронних мереж; підвищити оперативність обробки інформації в штучних нейронних мережа

Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)

Artificial neural networks make it possible to identify black-box models. Based on a recurrent nonlinear autoregressive exogenous neural network, this research provides a technique for simulating the static and dynamic behavior of a DC-DC power converter. This approach employs an algorithm for training a neural network using the inputs and outputs (currents and voltages) of a Buck converter. The technique is validated using simulated data of a realistic Simulink-programmed nonsynchronous Buck converter model and experimental findings. The correctness of the technique is determined by comparing the predicted outputs of the neural network to the actual outputs of the system, thereby confirming the suggested strategy. Simulation findings demonstrate the practicability and precision of the proposed black-box method

Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 202

Multi-step ahead response time prediction for single server queuing systems

Multi-step ahead response time prediction of CPU constrained computing systems is vital for admission control, overload protection and optimization of resource allocation in these systems. CPU constrained computing systems such as web servers can be modeled as single server queuing systems. These systems are stochastic and nonlinear. Thus, a well-designed nonlinear prediction scheme would be able to represent the dynamics of such a system much better than a linear scheme. A nonlinear autoregressive neural network with exogenous inputs based multi-step ahead response time predictor has been developed. The proposed estimator has many promising characteristics that make it a viable candidate for being implemented in admission control products for computing systems. It has a simple structure, is nonlinear, supports multi-step ahead prediction, and works very well under time variant and non-stationary scenarios such as single server queuing systems under time varying mean arrival rate. Performance of the proposed predictor is evaluated through simulation. Simulations show that the proposed predictor is able to predict the response times of single server queuing systems in multi-step ahead with very good precision represented by very small mean absolute and mean squared prediction errors

Data-Driven Disturbance Estimation and Control with Application to Blood Glucose Regulation

A data-driven control approach for nonlinear systems is proposed, called data-driven estimation and control (D2EC), which combines a disturbance estimator and a nonlinear control algorithm. The estimator provides a signal representing the unknown disturbances affecting the plant to control. This signal is used by the control algorithm to improve its performance. A real-data study is presented, concerned with the regulation of blood glucose concentration in type 1 diabetic patients. Preliminary tests of the D2EC approach are also carried out using a diabetic patient simulator, obtained from a revised version of the well-known University of Virginia/Padova model. Both the real-data and the simulator-based studies indicate that the proposed approach has the potential to become an effective tool in the context of diabetes treatment and, more in general, in the biomedical field, where accurate first-principle models can seldom be found and relevant disturbances are present

Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered Narx Neural Network Approach

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications

Resource Management in Computing Systems

Resource management is an essential building block of any modern computer and communication network. In this thesis, the results of our research in the following two tracks are summarized in four papers. The first track includes three papers and covers modeling, prediction and control for multi-tier computing systems. In the first paper, a NARX-based multi-step-ahead response time predictor for single server queuing systems is presented which can be applied to CPU-constrained computing systems. The second paper introduces a NARX-based multi-step-ahead query response time predictor for database servers. Both mentioned predictors can predict the dynamics of response times in the whole operation range particularly in high load scenarios without changes having to be applied to the current protocols and operating systems. In the third paper, queuing theory is used to model the dynamics of a database server. Several heuristics are presented to tune the parameters of the proposed model to the measured data from the database. Furthermore, an admission controller is presented, and its parameters are tuned to control the response time of queries which are sent to the database to stay below a predefined reference value.The second track includes one paper, covering a problem formulation and optimal solution for a content replication problem in Telecom operator's content delivery networks (Telco-CDNs). The problem is formulated in the form of an integer programming problem trying to minimize the communication delay and cost according to several constraints such as limited content replication budget, limited storage size and limited downlink bandwidth of each regional content server. The solution of this problem is a performance bound for any distributed content replication algorithm which addresses the same problem

Design of ensemble forecasting models for home energy management systems

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.info:eu-repo/semantics/publishedVersio