Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks

This paper deals with sensor fusion of magnetic, angular rate and gravity sensor (MARG). The main contribution of this paper is the sensor fusion performed by supervised learning, which means parallel processing of the different kinds of measured data and estimating the position in periodic and non-periodic cases. During the learning phase, the position estimated by sensor fusion is compared with position data of a motion capture system. The main challenge is avoiding the error caused by the implicit integral calculation of MARG. There are several filter based signal processing methods for disturbance and noise estimation, which are calculated for each sensor separately. These classical methods can be used for disturbance and noise reduction and extracting hidden information from it as well. This paper examines the different types of noises and proposes a machine learning-based method for calculation of position and orientation directly from nine separate sensors. This method includes the disturbance and noise reduction in addition to sensor fusion. The proposed method was validated by experiments which provided promising results on periodic and translational motion as well

Parameter Optimization of Deep Learning Models by Evolutionary Algorithms

Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning we usually apply neural networks with various structure. The algorithms of the deep learning techniques and the structure of the applied networks have several parameters that have a huge impact on the performance of the search technique. These parameters are called hyperparameters. The aim of our current research is to optimize these hyperparameters using evolutionary and swarm based optimization algorithms

Parameter Optimization of Deep Learning Models by Evolutionary Algorithms

Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning we usually apply neural networks with various structure. The algorithms of the deep learning techniques and the structure of the applied networks have several parameters that have a huge impact on the performance of the search technique. These parameters are called hyperparameters. The aim of our current research is to optimize these hyperparameters using evolutionary and swarm based optimization algorithms

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0:40 - 1:56% for the BGP model and 4:49 - 7:52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications

Számítási intelligencia algoritmusok, rendszerek és modellek = Algorithms, systems and models in computational intelligence

Korábbi eredményeinkre építve javasoltuk egy evolúciós (pl. bakteriális, részecskeraj) memetikus algoritmuscsaládot, az LM, maxi gradiens, és kombinációs eljárásokat alkalmaztunk lokális keresésre. Az új módszerek jobb konvergenciasebességgel és –pontossággal rendelkeznek, különösen a fuzzy modellek konstrukciójában. Javaslatot tettünk multipopulációs, többszálas és hibrid evolúciós, iteratív mohó és ütemezett vegyes evolúciós és memetikus eljárásokra. Szabványos adathalmazokon e módszerekkel az eddig publikált eredményeknél jobbat értünk el. Vizsgáltuk a fuzzy neurális hálózatokat, új struktúrákat, műveleteket bevezetve megkezdtük a hardver implementációt; fuzzy kognitív térképeket vizsgáltunk. Javaslatot tettünk a fuzzy szignatúrák geometriailag struktúrált általánosítására, változó finomságú szituációs térképek leírására. Javasoltuk a fuzzy 2 dimenziós raszterek alkalmazását a képreprezentációban. Az új komplex fuzzy - evolúciós/mohó/gradiens alapú optimalizációs - neurális hálózat eszközkészletet a műszaki és alkalmazott problémák széles körében használtuk fel, így a távközlési, a szállítási és logisztikai hálózatok optimalizációjára, hibadetektálásra; intelligens mobil robotok irányítására, kommunikációjára és autonóm együttműködésére; ellátási láncok és gyártási folyamatok optimalizálására; erőforrásallokációra és –ütemezésre; karakterfelismerésre és az építő- és környezetmérnöki döntéstámogatásra. | Based on our earlier research results we proposed a family of enhanced bacterial and evolutionary other memetic algorithms (e.g. Partical Swarm Optimization), with Levenberg-Marquard and Steepest Descent, viz. combinatorial methods for local search. The new methods have better convergence speed and accuracy, especially in fuzzy rule based model construction. We proposed multipopulation, multithread and hybrid evolutionary, iterative greedy and alternatingly scheduled mixed evolutionary and memetic approaches. We have achieved better results for standard benchmark data sets than any other authors. We studied neural networks based on fuzzy operations, proposing new structures, new operation families and starting hardware implementation; and we simulated fuzzy cognitive maps. We proposed extended classes of fuzzy signatures with geometric structure, modeling situational maps with flexible depth and fineness. We proposed fuzzy 2D grids for image representation. The new complex fuzzy - evolutionary/greedy/gradient optimization - neural network tool kit thus developed was deployed for a wide variety of engineering and applied problems, telecommunication, transport and logistic network optimization and failure detection, intelligent and mobile robot control, communication and co-ordination of autonomous collaboration; optimization of supply chains and production, resource allocation and scheduling, character recognition, and decision support in civil and environmental engineering

Fuzzy rendszerek és modellek elemzése és identifikációja = Analysis and identification of fuzzy systems and models

Új algoritmuscsaládot fejlesztettünk ki, a bakteriális memetikus algoritmusokat, a bakteriális evolúciós algoritmust globális, a Levenberg-Marquard algoritmust pedig lokális keresésként alkalmazva. Ez az eljárás jobb algoritmusokat eredményezett az ismert hasonló módszereknél a pontosság és a ciklusszám összefüggésében; ezt különböző referencia alkalmazások és más példák segítségével bizonyítottuk. Az alkalmazások másik csoportját a logisztika adta. Kiterjesztettük Kano minőségi modelljét fuzzy exponensekre, melyet BMA-val optimalizáltunk és megkezdtük az utazó ügynök probléma közelítő megoldásának vizsgálatát is. Megmutattuk, hogy a fuzzy szabályinterpoláció számos valós probléma megoldására alkalmas. Sikeresen foglalkoztunk komplex forgalomirányítási alkalmazásokkal, továbbá vasúti menetrend és késés miatti átütemezés kérdéskörével. Szoftverrendszert implementáltunk, mely nagyszámú fuzzy következtetési és irányítási algoritmus összehasonlítására alkalmas. Kiterjesztettük a fuzzy szignatúrákat hierarchikus struktúrákra is és a Mamdani algoritmusra is. A fuzzy szignatúrákat robotok mozgásirányítására és kommunikációjára alkalmaztuk. E robotokat szimulációs technikával és saját fejlesztésű hardver segítségével is vizsgáltuk. Új kutatási részterületet indítottunk el a fuzzy műveletek és a rajtuk alapuló fuzzy flip-floppok vizsgálatával, melyekből konnekcionista rendszereket hoztunk létre és e fuzzy neurális hálózatokat modellkonstrukcióra és approximációra alkalmaztuk. | We developed a new family of algorithms, the Bacterial Memetic Algorithms by combining the Bacterial Evolutionary Algorithm as a global search and the Levenberg-Marquard algorithm as a local search method. This approach provided better algorithms in terms of approximation accuracy and population cycles than other similar approaches in the literature, as it was evidenced by various benchmark and real life applications. Another group of successful applications is in the logistics area. We extended Kano’s quality model to fuzzy exponents, optimized by BMA, and we started to research for the approximate solution of the Traveling Salesman Problem.We showed that fuzzy rule interpolation could be deployed for a number of real application areas. We dealt with complex traffic control applications as well as with railway time table and delay triggered rescheduling problems successfully. We implemented a software for the comparison of a large number of fuzzy reasoning and control algorithms. We extended Fuzzy Signatures to both hierarchical structures and Mamdani’s algorithm. We applied Fuzzy Signatures for motion control and fuzzy communication of robots. Such robots were investigated both in simulation and hardware construction developed by ourselves. We started a new research sub-direction by analyzing fuzzy operators and fuzzy flip-flops based on them. We built connectionist systems from them and we used these fuzzy neural networks for model construction and approximation

Extraction of Daily Life Log Measured by Smart Phone Sensors Using Neural Computing

AbstractThis paper deals with the information extraction of daily life log measured by smart phone sensors. Two types of neural computing are applied for estimating the human activities based on the time series of the measured data. Acceleration, angular velocity, and movement distance are measured by the smart phone sensors and stored as the entries of the daily life log together with the activity information and timestamp. First, growing neural gas performs clustering on the data. Then, spiking neural network is applied to estimate the activity. Experiments are performed for verifying the effectiveness of the proposed method

Bead geometry modeling on uneven base metal surface by fuzzy systems for multi-pass welding

This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead’s local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width, contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight Vgroove by comparing the estimated bead characteristics with the measurements and calculating the accumulated error of the filled groove cross-section. The results show that the proposed model provides a good estimation of the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects

Supervised Learning with Small Training Set for Gesture Recognition by Spiking Neural Networks

This paper proposes a novel supervised learning algorithm for spiking neural networks. The algorithm combines Hebbian learning and least mean squares method and it works well for small training datasets and short training cycles. The proposed method is applied in human-robot interaction for recognizing musical hand gestures based on the work of Zoltán Kodály. The MNIST dataset is also used as a benchmark test to verify the proposed algorithm’s capability to outperform shallow ANN architectures. Experiments with the robot also provided promising results by recognizing the human hand signs correctly