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The paper is devoted to consider problems of project management in conditions of internal and external uncertainty in project state assessment. It is shown, that uncertainty of project state assessments influences negatively on the project and on the organized system, in which projects realize. The mechanisms of formalized project state assessment with means of artificial neural networks are proposed...
We adopt a data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds. Leveraging on the recent stability results for the inexact Iterative Projected Gradient (IPG) algorithm and by using the cover tree's ANN searches, we decrease the projection cost of the IPG...
In advanced wireless communication systems that require spectrally efficient modulation schemes, the modulated signal with a high peak-to-average power ratio (PAPR) drives the power amplifier (PA) to operate near the saturation region and introduces serious nonlinearity of the PA. Digital predistortion (DPD) is one of the most promising techniques for PA linearization. In this paper, we propose a...
The paper presents a adaptive dynamic surface control method for a class of strict-feedback nonlinear system based on neural network. In the previous adaptive neural networks control proposed using backstepping, the number and complexity of intermediate variables increase as the increasing order of the system. This makes it difficult to achieve learning for the high-order strict-feedback systems due...
With the evolution of internet, there has been an unprecedented and unlimited growth in volume, velocity, veracity and variety of the data and the complexity of data attributes is on the rise. Further, in the domain of internet, data is not geo-centric any longer and multiple locations are contributing to the data acquisition technologies including but not limited to packet captures, data logs, routing...
This article aims at updating the domains belonging to the parameters of the model resulting from the decomposition of the linear system on the Meixner-like basis. The robust identification is performed after applying the minimization of the Normalized Mean Square Error to estimate the optimal Meixner-like pole. In the context of set membership identification, the feasible parameter set is defined...
Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly...
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and contains multiple fields, a typicalrepresentation is to transform it into a high-dimensional sparsebinary feature representation via one-hot encoding...
This work attempts to find the most optimal setting for shallow artificial neural network (ANN) for Bengali digit dataset. Recognition of handwritten Bengali numerals has recently gained much interest among researchers due to significant performance gain found in the recognition of English numerals using artificial neural network. In this work, a new dataset of 70,000 samples were created first by...
This work attempts to find the most optimal setting for a convolutional neural network (CNN) for Bengali digit dataset classification. Recognition of handwritten Bengali numerals has recently gained much interest among researchers due to the significant performance gain found in the recognition of English numerals using neural network based architecture. In this work, a new dataset of 70,000 samples...
HEVC (high efficiency video coding), as the latest video coding standard, is more efficient than H.264/AVC, nevertheless it also brings in a very high computational complexity. To reduce the time of CU (coding unit) splitting or PU (prediction unit) mode deciding, a fast algorithm based on ANN (artificial neural network) and texture analysis is proposed in this paper. First, we acquire and then label...
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...
In this paper we present a novel approach to human-robot control. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, named KURE in this paper, with the ability to adapt both towards the user and the robotic morphology. The aim is for a transparent mechanism connecting user and robot, allowing for a seamless integration of control...
In this paper modified enhanced fuzzy min max (modified-EFMMN) has been proposed for pattern classification. The objectives of modified-EFMM are firstly, to lift the classification accuracy, secondly to reduce the network complexity and thirdly to utilize minimum number of features to provide classification decision. The modified-EFMM handles overlap among the different class hyperbox more stringently,...
The problem of finding nearest neighbours in terms of Euclidean distance, Hamming distance or other distance metric is a very common operation in computer vision and pattern recognition. In order to accelerate the search for the nearest neighbour in large collection datasets, many methods rely on the coarse-fine approach. In this paper we propose to combine Product Quantization (PQ) and binary neural...
This paper proposes a design methodology to optimize Artificial Neural Networks (ANN) modelling reflectarray cell. It is applied to a Phoenix cell with 5 inputs parameters. The results demonstrate that the final ANN model is reliable and accurate with an average phase error of 1,4°.
This paper proposes an intuitive yet simple machine learning (ML) approach that consist of two generic algorithms augmenting one another to solve problems they are not designed to solve. Since most machine learning algorithms are designed for a particular dataset or task, combining multiple ML algorithms can greatly improve the overall result by either helping tune one another, generalize, or adapt...
Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used...
Outlier analysis is an essential task in data science to find out inconsistencies in data to build a good classifier in better decision making. Outlier's detection from categorical data is a big task. Outliers from data ought to eliminate to model a better Classifier. While modeling categorical data, infrequent records which are less than the threshold value are treated as outliers and these outliers...
This is a study on the role of morphology (sensor configuration) and behavioral (control system) adaptation in simulated robot teams that must accomplish cooperative tasks. The research objective was to elucidate the necessary features and computational mechanics of a method that automates the behavior-morphology design of robot teams that must accomplish cooperative tasks (tasks that cannot be optimally...
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