Underwater robotic vehicles have become an important tool for various underwater tasks because they háve greater speed, endurance, depth capability, and safety than human divers. The problem of controlling a remotely operated underwater vehicle in 6 degrees of freedom (DOF) is addressed in this paper, as an example of a system containing severe non-linearities. Neural networks are been used in a closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the bounded-ness of signals are guaranteed by applying Barbalaťs Lyapunov-like lemma. In this páper, a neural network architecture based on radial basis functions has been ušed to evaluate the performance of the proposed adaptive controller for the motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV).
The paper preseiits an investigation on vibrations of mechanical systems arising from unbalanced masses. At the experimental stage, a power transmission shaft is driven at different operating speeds, therefore, the paranieters, such as displacement, velocity and acceleration in vertical direction due to body vibrations are measured at various points on the frame before and after balancing. Balancing has provided a definite decrease in the amplitudes of vibration parameters.
In addition to these studies mentioned above, the use of Neural Network (NN) for vibration analysis of a frame due to unbalanced transmission shaft is also achieved. The results show that the NN approach exactly follows the foregoing results. This implies the necessity of the non-linear modelling capabilities of the NN for vibration problems of mechanical systems.
This paper proposes a model of neural tree architecture with probabilistic neurons. These trees are used for classification of a large amount of computer grid resources to classes. The first tree is used for classification of hardware part of dataset. The second tree classifies patterns of software identifiers. Trees are implemented to successfully separate inputs into nine classes of resources. We propose Particle Swarm Optimization model for tasks scheduling in computer grid. We compared time of creation of schedule and time of makespan in six series of experiments without and with using neural trees. In experiments with using neural tree we gained the subset of suitable computational resources. The aim is effective mapping of a large batch of tasks into particular resources. On the base of experiments we can say that improvements have been made even for middle and small batch of tasks.
In this paper a classification system, which corisists of a neural network and a decision element, is presented, both parts processing information in series. For the neural network, we propose a training algorithm based on the direct equalization of weights and components of prototype vectors, and a neuronal function that detects similarities between its inputs and the weights. This systematics allows, in addition to a good performance in recognition, an easy, time-controlled reprogramming process of the network, even for large patterns. To test and validate the system, a real classifier is presented and studied, a classifier that is designed to recognize segmented handwritten characters corresponding to the NIST SD19 database and with which good results for digits and lower-case letters are obtained.
Liquefaction potential is a scientific assessment parameter to assess liquefaction of medium to fine grained cohesion-less soil due to earthquake shaking. In this paper alternative liquefaction potential prediction models have been developed using adaptive neuro fuzzy inference system (ANFIS) and multiple linear regression (MLR) technique. Geological survey of the study area was performed and forty locations were identified to perform standard penetration test (SPT). Disturbed and undisturbed soil samples were collected from the borehole to execute the laboratory tests. The bore-log datasets were used for determining liquefaction potential of the cohesion-less soils. The analytical approach by Idriss and Boulanger (I & B) has been applied initially to estimate liquefaction potential of soil on the basis of standard penetration test datasets obtained from the field investigations. To develop the ANFIS models 101 datasets were collected and incorporated for the development of fuzzy neural network models. Multiple linear regression (MLR) models have also been developed and the results were compared with neuro-fuzzy models. Based on obtained results it can be stated that the developed adaptive neuro fuzzy inference system models have better prediction ability to predict liquefaction potential with satisfactory level of confidence and can be used as an alternative tool.
A generalization of Nevanlinna’s First Fundamental Theorem to superharmonic functions on Green balls is proved. This enables us to generalize many other theorems, on the behaviour of mean values of superharmonic functions over Green spheres, on the Hausdorff measures of certain sets, on the Riesz measures of superharmonic functions, and on differences of positive superharmonic functions.
The least concave majorant, $\hat F$, of a continuous function $F$ on a closed interval, $I$, is defined by $ \hat F (x) = \inf\{ G(x) G \geq F, G \text{ concave}\},\quad x \in I. $ We present an algorithm, in the spirit of the Jarvis March, to approximate the least concave majorant of a differentiable piecewise polynomial function of degree at most three on $I$. Given any function $F \in\mathcal{C}^4(I)$, it can be well-approximated on $I$ by a clamped cubic spline $S$. We show that $\hat S$ is then a good approximation to $\hat F$. We give two examples, one to illustrate, the other to apply our algorithm., Martin Franců, Ron Kerman, Gord Sinnamon., and Obsahuje bibliografii
The main purpose of this paper is to give a new approach for partial metric spaces. We first provide the new concept of KM-fuzzy partial metric, as an extension of both the partial metric and KM-fuzzy metric. Then its relationship with the KM-fuzzy quasi-metric is established. In particularly, we construct a KM-fuzzy quasi-metric from a KM-fuzzy partial metric. Finally, after defining the notion of partial pseudo-metric systems, a one-to-one correspondence between partial pseudo-metric systems and KM-fuzzy partial pseudo-metrics is constructed. Furthermore, a fuzzifying topology τP on X deduced from KM-fuzzy partial metric is established and some properties of this fuzzifying topology are discussed.
We present a new approach to solving boundary value problems on noncompact intervals for second order differential equations in case of nonlocal conditions. Then we apply it to some problems in which an initial condition, an asymptotic condition and a global condition is present. The abstract method is based on the solvability of two auxiliary boundary value problems on compact and on noncompact intervals, and uses some continuity arguments and analysis in the phase space. As shown in the applications, Kneser-type properties of solutions on compact intervals and a priori bounds of solutions on noncompact intervals are key ingredients for the solvability of the problems considered, as well as the properties of principal solutions of an associated half-linear equation. The application of this method leads to some new existence results, which complement and extend some previous ones in the literature.