In recent years, there is an increasing need to monitor k nearest neighbor (k-NN) in a road network. There are existing solutions on either monitoring k-NN objects from a single query point over a road network, or computing the snapshot k-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is...

The isospin splitting of the in-medium $NN\rightarrow N\Delta$ cross sections in asymmetric nuclear medium are investigated framework one-boson exchange model by including $\delta$ and $\rho$ mesons. Our results show that correction factors $R=\sigma_{ NN\rightarrow N\Delta}^*/\sigma_{NN\rightarrow N\Delta}^{\text{free}}$ have $R_{pp \to n\Delta ^{++}} < R_{nn p\Delta ^{-}}$ $R_{NN N\Delta ^{+}...

The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the k-NN classifier. In this thesis we investigate the use of alternative distances for the k-NN classifier. We start by introducing some background notions in statistical machine learning. We defi...

• Needed Narrowing (NN) is the standard operational mechanism of functional logic languages. • The definition of NN makes use of the notion of a definitional tree. • A Definitional tree is a structure which contains all the information about the program rules defining a function and guides the computation. • A great effort has been done to provide these languages with high level implementations...

Neural Network (NN) is a supervised machine learning technique, which is typically employed to solve classification problems. When solving a classification problem with the conventional NN, the input data fed into the NN often consists of multiple attributes of various properties. However, training the NN with all of the available input attributes may not lead to the desirable performance consi...

It is well known that computed torque robot control is subjected to performance degradation due to uncertainties in robot model, and application of neural network(NN) compensation techniques are promising. In this paper we examine the eeectiveness of NN as a compensator for the complex problem of Cartesian space control. In particular we examine the diierences in system performance when the sam...

This paper presents Neural Network (NN) model of Polymer Electrolyte Membran (PEM) Fuel Cell for electric vehicle. The NN model simplifies the conventional model that considered thermodynamics, electrochemistry, hydrodynamics and mass transfer theory. The NN has a multilayer feed forward network structure and is trained using a back propagation learning rule. The NN model is used to predict the...

The field of neurocontrol, in which neural networks are used for control of complex systems, has many potential applications. One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks ...

In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed a...

Neural networks (NN) have become to be general tools for modeling functional relationships in engineering. They are used to model the behavior of products and the properties of processes. Nevertheless, their use is often ad hoc. This paper provides a sound basis for using NN as tools for modeling functional relationships implicit in empirical engineering data. First, a clear deenition of a mode...