Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Voting based Extreme learning machine was recently proposed to reduce the error due to variance in Extreme Learning Machine. This paper further refines the algorithm by using entropy based ensemble pruning. Results obtained shows significant improvement in performance along with reduction in computational and storage requirement.
The automotive industry is constantly looking for alternative solutions to reduce manufacturing cost and use renewable materials. Implementing agro-fibres as polymer fillers in thermoplastic matrix will satisfy the automotive criteria without sacrificing the mechanical properties currently set by the conventional fillers such as glass fibre, talc, or mica. This paper proposes the use of wheat straw...
The sigmoid activation function cast-off to convert the equal of activation of units (neurons) in the output indicator. There are a numeral of mutual tasks in activation with the use of artificial neural networks (ANN). The maximum communal use of manifold functions to Multi Layered Perceptron (MLP) and the transmission of professions in research and engineering. However, given the wide range of problematic...
The paper describes system identification by using Artificial Neural Networks that is applied to a permanent magnet DC motor. To identify its dynamic behavior an experimental setup has been developed that enables to measure data of the system input (armature voltage) and output (current and rotor speed). Generally, the identification methods can be classified as parametric and non-parametric. We use...
The use of artificial neural networks has enabled applications that would be impossible to achieve with conventional electronics, through to versatility that them have, they can be configured as needed and use as required such as classifiers, adaptive filters, controllers and predictors. This paper describes a experimental implementation of virtual speed sensor for DC motor using back-propagation...
The present work investigated the potential of Artificial Neural Network (ANN) model to estimate Global Solar radiation on tilted surface (GSRT) from the horizontal ones. The collected experimental data (from meteorological station located in renewable energy development center of Algiers) were divided in to two different subsets as follows training and testing subsets. The training subset was selected...
This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA). The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application. There are many algorithms applied to this computer vision problem and many...
In this paper, a maximum sensibility neural network is proposed to make an online learning system of a inverse controller of a plant. This neural network is trained to learn the response of the plant to different random inputs. Once the network is trained, it can be used to control the plant to a desired output.
In the present article was implemented a maximum sensibility neural network in a reconfigurable logical electronic structure (cell) in which different basic logical functions and combinational logic circuits as comparators, multiplexers and encoders are obtained. This neural network has advantages like easy implementation and a quick learning based on manipulation of the information in place of a...
Civil structures are known for having a non-linear and time-variant behavior, these features make a challenging task the use of linear methods for modeling the dynamical behavior since they only model time-invariant systems. To overcome this limitation, several approaches based on non-parametric methods have been proposed, however, the selection of the best-suited method for a particular case can...
This paper investigates the design of a multivariate B-spline neural network using the orthogonal least squares algorithm for non-linear system identification. The B-spline neural network is a type of basis function neural network which has been developed from the function approximation approach based on B-spline functions. Usually, this kind of neural network is trained using the gradient-based algorithm...
The issue of PM2.5 is becoming a popular atmospheric research hotpot recently. This particular paper evaluates the era reasons as well as influencing factors associated with PM2.5 based on the information associated with PM2.5 in Xing Tai (2014. 01. 01 - 2014. 04. 26), and builds the actual era as well as evolution mode of PM2.5 in Xing Tai by utilizing evolutionary algorithms formula and BP neuron...
Predictive analytics of the traffic flow is paid more attention by the traffic engineering experts and relevant departments. However, how to forecast traffic volume still is an important problem affecting the traffic theoretical and practical analysis. Firstly, this paper set up a three layers BP neural network basing on the actual situation to introduce the modeling process of the neural network...
Health care practitioners need to diagnose a disease and make a decision about the treatments. This has been one of the most challenging tasks for them. During the last two decades, researchers from computer science, mathematics, and medical sciences have been developing intelligent tools for supporting medical decision making. Various soft computing based systems have been successfully developed...
Cognitive radio networks (CRNs) or self-organizing mobile cellular networks are a promising technology for 5G that manages the spectrum frequency domain more efficiently. At the heart of CRNs is the cognitive engine (CE), which is responsible for decision making on the optimal configuration settings for the CRN in real time if possible. In this paper a novel paradigm for decision making in the CE...
The purpose of analyzing gene network structure is to identify and understand some unknown related functions and the regulatory mechanisms at molecular level in organisms. Traditional model of the gene regulatory networks often lack an effective method of solving with gene expression profiling data because of high time and space complexity. In this study, a new model of gene regulatory network based...
Memristors promise higher device density and design flexibility. Besides utilizing memristors for digital memory, another promising avenue for adoption is the advancement of neural network circuits capable of learning. Neural network implementations with memristors have been proposed, including memristor synaptic training methodologies. This work highlights applications of a neural learning methodology...
In this paper, a novel vessel maneuvering model (VMM) based on multi-output dynamic fuzzy neural networks is proposed. Data samples are generated from the vessel maneuvering dynamics based on a group of well established nonlinear differential equations. Reasonably, the vessel dynamics identification becomes recurrent prediction since the desired multi-output states are considered to be dependent on...
An artificial neural network model based on dendritic computation using two lattice metrics is introduced in this paper. A description of the mathematical framework of the proposed model is provided and its corresponding learning algorithm is presented in mathematical pseudocode. Computational experiments are given to demonstrate the effectiveness and performance of the learning algorithm as well...
By combining two fast training methods, i.e., the weights-direct-determination (WDD) method and Levenberg-Marquardt method, this paper proposes a novel training algorithm called weights and structure policy (WASP) for the three-layer feedforward neuronet, in addition to the algorithm of weights and structure determination (WASD). Note that the pruning-while-growing and second-pruning techniques are...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.