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In this paper, finite horizon optimal control and communication co-design problem has been investigated for uncertain networked control system (NCS) with limited transmit power. Firstly, the mathematical relation between network imperfections (e.g. network-induced delays and packet dropouts) and practical wireless communication channel quality has been studied. Then, a novel networked control system...
This paper considers the identification problem of nonlinear systems based on single-hidden-layer neural networks (SHLNNs) and Lyapunov theory. A nonlinearly parameterized neural model, whose weights are adjusted by robust adaptive laws, which are designed via Lyapunov theory, is proposed for ensuring the convergence of the residual state error to an arbitrary neighborhood of zero. In addition, a...
In this paper, we present a new technique for model order reduction (MOR) that is based on an artificial neural network (ANN) prediction. The ANN-based MOR can be applied for different scale systems with substructure preservation. In the proposed technique, the ANN is implemented for predicting the unknown elements of the reduced order model. Prediction of the ANN architecture is based on minimizing...
Forecasts combinations normally use point forecasts that were obtained from different models or sources ([1], [2],[3]). This paper explores the incorporation of internal structure parameters of feed-forward neural network (NN) models as anapproach to combine their forecasts via ensembles. First, the generated NN models that could be part of the ensembles are subjectto a clustering algorithm that uses...
A new hybrid approach for Petri nets (PNs) is proposed in this paper by combining the PNs principles with the foundations of information theory for knowledge representation. The resulting PNs have been named Plausible Petri nets (PPNs) mainly because they can handle the evolution of a discrete event system together with uncertain (plausible) information about the system using states ofinformation...
Kernel methods and neural networks (NN) are two of the most powerful tools of machine learning to solve the engineering and science problems. In this paper, we propose kernel ridge regression (KRR) and NN to estimate the compressive strength (CS) of concrete with recycled aggregate based on the values of cement, natural aggregate, recycled aggregate, sand, and water. We collected a dataset of 182...
As demonstrated earlier, the learning effectiveness and learning speed of single-hidden-layer feedforward neural networks are in general far slower than required, which has been a major bottleneck for many applications. Huang et al. proposed extreme learning machine (ELM) which improves the training speed by hundreds of times as compared to its predecessor learning techniques. This paper offers an...
In this communication we explain how a Support Vector Machine (SVM) can be applied to compute the Euler number or Genus of a 2-D binary image. By taking into account the results provided by a mathematical formulation that is known producing exact results we derive two specialized SVM-based architectures, one useful for the 4-connected case and one useful for the 8-connected case. We validate the applicability...
This paper aims to solve the optimal power and gas flow problem of the integrated electricity and natural gas networks. Gas-fired power plants provide linkage between electricity and natural gas networks. The model of natural gas network consisting of gas sources, loads, pipelines and compressors is calculated by the Newton-Raphson method. Afterwards, a multi-objective group search optimizer with...
Improving bicycle safety is considered as a growing concern for two reasons. First, in the United States in recent years, about 700 cyclists were killed and about 48,000 were injured in bicycle motor vehicle crashes each year. Regarding crash location, from 2008 to 2012 in the United States, more than 30% of cyclist fatalities occurred at intersections. Furthermore, up to 16% of bicycle-related crashes...
The paper adopts the state-of-the-art machine learning deep neural network to model the evolution of the traffic state along a 21.1 miles long stretch of the I-15 highway. The built model is used for short-term prediction of the traffic states. The 21.1 miles stretch is divided into 43 segment. Building a predictive model for this stretch is a multivariate problem where the responses are the speeds/flows...
The paper proposes a new method based on artificial neural network (ANN) for estimation of pressure loss coefficients in Tee Junction for dividing flows. The selected features are given as input to the ANN including flow and geometry parameters of Tee Junction for training and pressure loss coefficients are estimated in an efficient manner. The paper also gives comparison results of ANN based approach...
In this paper, new polynomial and neural network models for power amplifier digital pre-distortion are introduced. The motivation behind the suggested models is having low complexity models that maintain good error performances. Also, this paper discusses the comparison between polynomial and neural network models in terms of model complexity and error performance before and after applying a compressed...
This paper introduces a maximum power point method (MPPT) based on an artificial neural network (ANN) and a scanning algorithm. The proposed MPPT is applied to pumping system. The system is composed of solar generator, boost converter and centrifugal pump load driven by a DC motor. A complete simulation of the system is developed in MATLAB/SIMULINK under different weather conditions. The results obtained...
In the last decade, many advances have been made in the field of automatic temperature estimation, including wearable sensor technologies (WST), infrared thermography (IRT), and non-contact infrared thermometer (NCIT). In contrast with the WST and IRT, NCIT is inexpensive without the risk of potential skin irritation. Nevertheless, NCIT is limited in short valid estimation distance (<12 cm), resulting...
Economy trend (eco-trend) is the most important factor for developing the country. Unfortunately, various inevitable and unpredictable factor causes an effect on economic trend while the Natural Disaster period happened. The fluctuation of the trend is then occurred and make it more difficult to forecast. According to this research, the analysing method of the eco-trends prediction was represented...
In this paper, it is proposed a neural network based on by AutoAssociative Pyramidal Neural Network and their architecture, which uses concepts of receptive fields and autoassociative memory. These concepts are widely used in models of artificial neural networks and were incorporated into model proposed in this work. Furthermore, the proposed neural network also uses the concept of sharing weights...
This paper introduces a novel method, based on Gaussian Markov Random Field Model with back-propagation learning algorithm to retrieve multi-spectral satellite color imagery. The proposed method segregates the texture part and structure part of the imagery, and extracts features in the texture and structure parts separately. The extracted features are formed as a feature vector. The feature vector...
This paper is describing Artificial Neural Network (ANN) technique using a nonlinear speed controller design Permanent-Magnet-Synchronous-Motor (PMSM) methodology, where more emphasis is given to the tuning of the PID controller. Subsequently, speed control for PMSM was analyzed in depth using ANN techniques to enhance the performance parameters in terms of integral-gain (Ki), derivative-gain (Kd)...
This paper discusses how to apply a hybrid computation process comprised by three techniques of Artificial Neural Network (ANN), Bayesian probability and Cellular Automata (CA), to establish simulating model which can embody the advantages of perceptron, stochastic and dynamic. Through our hybrid computation process, we can perform a series of dynamic simulations with high accuracy. Additionally,...
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