Temat: Neural Networks - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Nonlinear position estimators based on artificial neural networks for low costs manufacturing systems
Autorzy:: Dogruer, C.
Kilic, E.
Dolen, M.
Koku, B.
Powiązania:: https://bibliotekanauki.pl/articles/384449.pdf
Data publikacji:: 2007
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: neural networks
esimators
Opis:: The accurate contral of CNC machine axis requires relatively expensive direct measurement sensors. In this paper, artificial neural network based position error estimators are comparatively evaluated as a part of a low-cost (but high performance) manufacturing system. Such schemes are very effective when the system is rot subjected to external loads as well as widely changing operating conditions such as ambient temperature.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 2; 40-44
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Inversion of fuzzy neural networks for the reduction of noise in the control loop for automotive applications
Autorzy:: Nentwig, M.
Mercorelli, P.
Powiązania:: https://bibliotekanauki.pl/articles/384669.pdf
Data publikacji:: 2009
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: neural networks
fuzzy control
inversion of neural networks
automotive control
noise reduction
Opis:: A robust throttle valve control has been an attractive problem since throttle by wire systems were established in the mid-nineties. Control strategies often use a feed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modelling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 3; 83-89
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Signature recognition with a hybrid approach combining modular neural networks and fuzzy logic for response integration
Autorzy:: Beltrán, M.
Melin, P.
Trujillo, L.
Lopez, M.
Powiązania:: https://bibliotekanauki.pl/articles/384541.pdf
Data publikacji:: 2010
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: pattern recognition
neural networks
fuzzy logic
Opis:: This paper describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 1; 20-27
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Suboptimal Non-linear Predictive Control Based on MLP and RBF Neural Models with Measured Disturbance Compensation
Autorzy:: Ławryńczuk, M.
Powiązania:: https://bibliotekanauki.pl/articles/384285.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: predictive control
neural networks
linearisation
quadratic programming
Opis:: This paper is concerned with a computationally efficient (suboptimal) non-linear Model Predictive Control (MPC) algorithm based on two types of neural models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF) structures. The model takes into account not only controlled but also the uncontrolled input of the process, i.e. the measured disturbance. The algorithm is computationally efficient, because it results in a quadratic programming problem, which can be effectively solved on-line by means of a numerically reliable software subroutine. Moreover, the algorithm gives good closed-loop control performance, comparable to that obtained in the fully-fledged non-linear MPC technique, which hinges on non-linear, usually non-convex optimisation.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 2; 54-64
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Optimization of a modular neural network for pattern recognition using parallel genetic algorithm
Autorzy:: Cárdenas, M.
Melin, P.
Cruz, L.
Powiązania:: https://bibliotekanauki.pl/articles/384887.pdf
Data publikacji:: 2011
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: modular neural networks
parallel genetic algorithm
multi-core
Opis:: In this paper, the implementation of a Parallel Genetic Algorithm (PGA) for the training stage, and the optimi zation of a monolithic and modular neural network, for pattern recognition are presented. The optimization con sists in obtaining the best architecture in layers, and neu rons per layer achieving the less training error in a shor ter time. The implementation was performed in a multicore architecture, using parallel programming techniques to exploit its resources. We present the results obtained in terms of performance by comparing results of the training stage for sequential and parallel implementations.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 1; 77-84
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Optimization of convolutional neural networks using the fuzzy gravitational search algorithm
Autorzy:: Poma, Yutzil
Melin, Patricia
González, Claudia I.
Martínez, Gabriela E.
Powiązania:: https://bibliotekanauki.pl/articles/384794.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: neural networks
convolutional neural network
fuzzy gravitational search algorithm
deep learning
Opis:: This paper presents an approach to optimize a Convolutional Neural Network using the Fuzzy Gravitational Search Algorithm. The optimized parameters are the number of images per block that are used in the training phase, the number of filters and the filter size of the convolutional layer. The reason for optimizing these parameters is because they have a great impact on performance of the Convolutional Neural Networks. The neural network model presented in this work can be applied for any image recognition or classification applications; nevertheless, in this paper, the experiments are performed in the ORL and Cropped Yale databases. The results are compared with other neural networks, such as modular and monolithic neural networks. In addition, the experiments were performed manually, and the results were obtained (when the neural network is not optimized), and comparison was made with the optimized results to validate the advantage of using the Fuzzy Gravitational Search Algorithm.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 109-120
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Hybrid intelligent system for pattern recognition
Autorzy:: Melin, P.
Castillo, O.
Powiązania:: https://bibliotekanauki.pl/articles/384459.pdf
Data publikacji:: 2007
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: soft computing
intelligent system
algorithms
fuzzy logic
neural networks
Opis:: We describe in this paper a general overview oj the analysis and design of hybrid intelligent systems for pattern recognition applications. Hybrid intelligent systems can be developed by a careful combination of several soft-computing techniques. The combination of soft computing techniques has to take advantage of the capabilities of each technique in solving port of the pattern recognition problem. We review the problems of face, fingerprint and mice recognition and their soiution using hybrid intelligent systems. Recognition rates achieved with the hybrid approaches are comparable with the best approaches known for solving these recognition problems.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 2; 13-19
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Response Integration in Ensemble Neural Networks using The Sugeno Integral and Fuzzy Inference System for Pattern Recognition
Autorzy:: Lopez, M.
Melin, P.
Powiązania:: https://bibliotekanauki.pl/articles/384555.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: ensemble neural networks
fuzzy logic
pattern recognition
fingerprint recognition
Opis:: Combining the outputs of multiple neural networks has been used in Ensemble architectures to improve the decision accuracy in many applications fields, including pattern recognition, in particular for the case of fingerprints. In this paper, we describe a set of experiments performed in order to find the optimal individual networks in terms of the architecture and training rule. In the second step, we used the fuzzy Sugeno Integral to integrate results of the ensemble neural networks. This method combines objective evidence in the form of the network's outputs, with subjective measures of their performance. In the third step, we used a Fuzzy Inference System for the decision process of finding the output of the ensemble neural networks, and finally a comparison of experimental results between Fuzzy Sugeno Integral and the Fuzzy Inference System are presented.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 52-58
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Coordinating the motion of mobile robots using Cellular Neural Network
Autorzy:: Siemiątkowska, B.
Powiązania:: https://bibliotekanauki.pl/articles/384283.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: neural networks
mobile robot
mapping
navigation
multi-robots systems
cellular neural network
Opis:: This paper presents a method for motion planning for a group of mobile robots. The goal of the group is to move through an environment and to reach a destination while maintaining the desired formation. The map of the environment is represented as a grid of cells. A state of each cell is determined. It can be free, occupied by the obstacle, occupied by a robot. The trajectories of the robots are planned using the modification of diffusion method. The algorithm is implemented using Cellular Neural Network. This kind of implementation allows of efficient path planning and to solve conflicts between robots. Computer simulations were preformed in order to proof the efficiency of the approach.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 2; 65-69
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Neural Based Autonomous Navigation of Wheeled Mobile Robots
Autorzy:: Al-Sagban, M.
Dhaouadi, R.
Powiązania:: https://bibliotekanauki.pl/articles/384293.pdf
Data publikacji:: 2016
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: reactive navigation
obstacle avoidance
autonomous ground robots
recurrent neural networks
Opis:: This paper presents a novel reactive navigation algorithm for wheeled mobile robots under non-holonomic constraints and in unknown environments. Two techniques are proposed: a geometrical based technique and a neural network based technique. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment by modulating its steering angle and turning radius. The dimensions and shape of the robot are incorporated to determine the set of all possible collision-free steering angles. The algorithm then selects the best steering angle candidate. In the geometrical navigation technique, a safe turning radius is computed based on an equation derived from the geometry of the problem. On the other hand, the neural-based technique aims to generate an optimized trajectory by using a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The experimental results demonstrate that the algorithms are capable of driving the robot safely across a variety of indoor environments.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 2; 64-72
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Face detection in color images using skin segmentation
Autorzy:: Hajiarbabi, M.
Agah, A.
Powiązania:: https://bibliotekanauki.pl/articles/384677.pdf
Data publikacji:: 2014
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: skin detection
neural networks
face detection
skin segmentation
image processing
Opis:: Face detection which is a challenging problem in computer vision, can be used as a major step in face recognition. The challenges of face detection in color images include illumination differences, various cameras characteristics, different ethnicities, and other distinctions. In order to detect faces in color images, skin detection can be applied to the image. Numerous methods have been utilized for human skin color detection, including Gaussian model, rule-based methods, and artificial neural networks. In this paper, we present a novel neural network-based technique for skin detection, introducing a skin segmentation process for finding the faces in color images.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2014, 8, 3; 41-51
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and its Optimization with Genetic Algorithms
Autorzy:: Hidalgo, D.
Castillo, O.
Melin, P.
Powiązania:: https://bibliotekanauki.pl/articles/384559.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: modular neural networks
type-2 fuzzy logic
pattern recognition
genetic algorithms
Opis:: We describe in this paper a comparative study between Fuzzy Inference Systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 59-73
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 13.

Tytuł:: I/Q Imbalance Compensation Algorithm based on Neural Networks
Autorzy:: Kirei, B. S.
Topa, M.
Neag, M.
Onet, R. C.
Powiązania:: https://bibliotekanauki.pl/articles/385018.pdf
Data publikacji:: 2009
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: low-IF receiver
I/Q imbalance compensation
image rejection
neural networks
Opis:: This paper proposed an I/Q imbalance compensation algorithm based on neural networks, suitable for low-IF receivers. First, the low-IF receiver architecture and the phenomena of I/Q imbalance (also referred as image interference) are described. The standard solution - using a complex LMS adaptive filter, which separates the desired, and image signals - is limited in that the recovered signal remains affected by the I/Q imbalance; the filter proposed here corrects this drawback. The functionality, convergence and stability of the neural network based filter are demonstrated through extensive computer simulations. A sizing example is also given - deduction of the number of sample necessary in order to achieve a -60 dB image rejection - along with the time domain behaviour of the resulting neural network.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 2; 66-71
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 14.

Tytuł:: Wykorzystanie sieci neuronowych w diagnostyce poprawności wykonania płytek drukowanych
Utilization of neural networks in process of diagnosis of correctness of assembling the printed circuit-boards
Autorzy:: Sikora, M.
Grochowski, M.
Powiązania:: https://bibliotekanauki.pl/articles/277213.pdf
Data publikacji:: 2011
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: diagnostyka
przetwarzanie obrazów
sztuczne sieci neuronowe
diagnostics
image processing
neural networks
Opis:: Artykuł opisuje stanowisko badawcze do diagnostyki optycznej poprawności wykonania płytek drukowanych przesuwających się po taśmie produkcyjnej. Diagnostyka optyczna realizowana jest za pomocą kamery. Obraz z kamery przekazywany jest do komputera PC, gdzie trafia do zaprojektowanego systemu diagnostycznego, zaimplementowanego w środowisku MATLAB. Po wstępnym przetworzeniu obrazy kierowane są do właściwego systemu diagnostycznego wykorzystującego sztuczne sieci neuronowe, który podejmuje ostateczną decyzję o poprawności montażu elementów płytki drukowanej. Cała aplikacja zrealizowana jest w środowisku MATLAB. W artykule zamieszczono wybrane wyniki badań analizujących wpływ aspektów takich, jak rodzaj oświetlenia, sposób obróbki i kompresji obrazu, dobór architektury i parametrów sieci neuronowej na jakość osiąganych wyników.
The paper describes research test stand that is used for optical diagnostics of correctness of assembling of printed circuit-board that moves on a tape. Optical diagnostics is carried out by camera, the images are transferred to computer PC and then to designed diagnostic system implemented in Matlab. After processing of the images they are analyzed by neural networks and the decisions about the correctness of assembling the elements on printed circuit-board are made. The whole application is designed in Matlab environment. The paper presents selected results describing researches carried out in the field of: illumination, image processing techniques, structures and parameters of neural networks and their influence on efficiency of the described system.
Źródło:: Pomiary Automatyka Robotyka; 2011, 15, 2; 49-54
1427-9126
Pojawia się w:: Pomiary Automatyka Robotyka
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 15.

Tytuł:: Neurocontrolled car speed system
Autorzy:: Nakonechnyi, Markiyan
Ivakhiv, Orest
Świsulski, Dariusz
Powiązania:: https://bibliotekanauki.pl/articles/27314203.pdf
Data publikacji:: 2022
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: neural controller
PID-algorithm of control
dynamic object
neural networks
electric car
speed control
Opis:: The features of the synthesis of neural controllers for the car speed control system are considered in this article. The task of synthesis is to determine the weight coefficients of neural networks that provide the implementation of proportional and proportional-integralderivative control laws. The synthesis of controllers is based on an approach that uses a reversed model of the standard. A model of the car speed control system with the use of permitting subsystems has been developed, with the help of the synthesized controller that is connected under certain specified conditions. With the iterative programming and mathematical modeling environment in MATLAB, and using the Simulink package, a structural scheme for controlling the speed of the car was constructed and simulated using synthesized neural controllers.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 13--21
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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