7,909 research outputs found
Designing Algorithms for Optimization of Parameters of Functioning of Intelligent System for Radionuclide Myocardial Diagnostics
The influence of the number of complex components of Fast Fourier transformation in analyzing the polar maps of radionuclide examination of myocardium at rest and stress on the functional efficiency of the system of diagnostics of pathologies of myocardium was explored, and there were defined their optimum values in the information sense, which allows increasing the efficiency of the algorithms of forming the diagnostic decision rules by reducing the capacity of the dictionary of features of recognition.The information-extreme sequential cluster algorithms of the selection of the dictionary of features, which contains both quantitative and category features were developed and the results of their work were compared. The modificatios of the algorithms of the selection of the dictionary were suggested, which allows increasing both the search speed of the optimal in the information sense dictionary and reducing its capacity by 40 %. We managed to get the faultless by the training matrix decision rules, the accuracy of which is in the exam mode asymptotically approaches the limit.It was experimentally confirmed that the implementation of the proposed algorithm of the diagnosing system training has allowed to reduce the minimum representative volume of the training matrix from 300 to 81 vectors-implementations of the classes of recognition of the functional myocardium state
Improving the Evolutionary Coding for Machine Learning Tasks
The most influential factors in the quality of the solutions
found by an evolutionary algorithm are a correct coding of the
search space and an appropriate evaluation function of the potential
solutions. The coding of the search space for the obtaining of decision
rules is approached, i.e., the representation of the individuals of
the genetic population. Two new methods for encoding discrete and
continuous attributes are presented. Our “natural coding” uses one
gene per attribute (continuous or discrete) leading to a reduction in
the search space. Genetic operators for this approached natural coding
are formally described and the reduction of the size of the search
space is analysed for several databases from the UCI machine learning
repository.ComisiĂłn Interministerial de Ciencia y TecnologĂa TIC1143–C03–0
Optimal Phase Swapping in Low Voltage Distribution Networks Based on Smart Meter Data and Optimization Heuristics
In this paper a modified version of the Harmony Search algorithm is proposed as a novel tool for phase swapping in Low Voltage Distribution Networks where the objective is to determine to which phase each load should be connected in order to reduce the unbalance when all phases are added into the neutral conductor. Unbalanced loads deteriorate power quality and increase costs of investment and operation. A correct assignment is a direct, effective alternative to prevent voltage peaks and network outages. The main contribution of this paper is the proposal of an optimization model for allocating phases consumers according to their individual consumption in the network of low-voltage distribution considering mono and bi-phase connections using real hourly load patterns, which implies that the computational complexity of the defined combinatorial optimization problem is heavily increased. For this purpose a novel metric function is defined in the proposed scheme. The performance of the HS algorithm has been compared with classical Genetic Algorithm. Presented results show that HS outperforms GA not only on terms of quality but on the convergence rate, reducing the computational complexity of the proposed scheme while provide mono and bi phase connections.This paper includes partial results of the UPGRID project. This project has re-
ceived funding from the European Unions Horizon 2020 research and innovation
programme under grant agreement No 646.531), for further information check
the website: http://upgrid.eu. As well as by the Basque Government through
the ELKARTEK programme (BID3A and BID3ABI projects)
Reactive with tags classifier system applied to real robot navigation
7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules
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