The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral skew shall be investigated. This paper focuses on realdata based modelling of the railway stand by various neural network models, i.e; linear neural unit and quadratic neural unit architectures. Furthermore, training methods of these neural architectures as such, real-time-recurrent-learning and a variation of back-propagation-through-time are examined, accompanied by a discussion of the produced experimental results

This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal.

This research is focused on solving problems in the area of software project management using metaheuristic search algorithms and as such is research in the field of search based software engineering. The main aim of this research is to evaluate the performance of different metaheuristic search techniques in resource allocation and scheduling problems that would be typical of software development projects. This paper reports a set of experiments which evaluate the performance of three algorithms, namely simulated annealing, tabu search and genetic algorithms. The experimental results indicate that all of the metaheuristics search techniques can be used to solve problems in resource allocation and scheduling within a software project. Finally, a comparative analysis suggests that overall the genetic algorithm had performed better than simulated annealing and tabu search

Sudoku puzzles are an excellent testbed for evolutionary algorithms.The puzzles are accessible enough to be enjoyed by people.However the more complex puzzles require thousands of iterations before a solution is found by an evolutionary algorithm. If we were attempting to compare evolutionary algorithms we could count their iterations to solution as a indicator of relative efficiency. However all evolutionary algorithms include a process of random mutation for solution candidates.I will show that by improving the random mutation behaviours I was able to solve problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at times more effective at solving the harder problems than the evolutionary algorithms. This implies that the quality of random mutation may have a significant impact on the performance of evolutionary algorithms with sudoku puzzles.Additionally this random mutation may hold promise for reuse in hybrid evolutionary algorithm behaviours

A brain computer interface (BCI) is a novel communication system that translates brain signals into control commands. In this paper, we present a P300 BCI system based on ordinal pattern features. Compared to BCI system based on linear time domain features, we have shown that slightly better classification accuracies and bitrates can be achieved for healthy and disabled subjects

This paper presents a solution to real-world delivery problems for home delivery services where a large number of roads exist in cities and the traffic on the roads rapidly changes with time. The methodology for finding the shortest-travel-time tour includes a hybrid meta-heuristic that combines ant colony optimization with Dijkstra's algorithm, a search technique that uses both real-time traffic and predicted traffic, and a way to use a real-world road map and measured traffic in Japan. Experimental results using a map of central Tokyo and historical traffic data indicate that the proposed method can find a better solution than conventional methods

In the process of motion magnetically levitated planar actuators, the magnetic flux through the coils been changed as the position changed, It produced inductive electromotive force which associated with the location and speed of coils. Induction electromotive force to produce induced current in the coils, which influence the stability of the actuators. This paper presents a analytical model of induction electromotive force in the coils of magnetically levitated a planar actuators, According to the analytical model it could predict control the compensation of drive currents in order to offset the induced current which produce by electromotive force, the stability of Magnetically levitated planar actuators have been improved. Finite element simulation had been used in this paper to approve the correct of the analytical model

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes' theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available

We describe a system that provides what we call all things considered support to a user. The core feature of this system is that it finds a balance between the satisfaction of short term (local) preferences and the satisfaction of long term (global) preferences. By operating according to both local and global standards the system serves a debiasing function - it produces recommendations that bypass the common tendency that people have of granting excessive weight to utilities that relate to the short term. The novelty of this system is that for every decision it has to make it considers a user's interests all things considered; it incorporates that user's local interests as well as his global interests

Multi-mode resource-constrained project scheduling problems (MRCPSPs) are classified as NP-hard problems, in which a task has different execution modes characterized by different resource requirements. Estimation of distribution algorithm (EDA) has shown an effective performance for solving such real-world optimization problems but it fails to find the desired optima. This paper integrates a novel hybrid local search technique with EDA to enhance their local search ability. The new local search is based on delete-then-insert operator and a random walk (DIRW) to enhance exploitation abilities of EDA in the neighborhoods of the search space. The proposed algorithm is capable to explore and explocit the search mechanism in the search space through its outer and inner loops The proposed algorithm is tested and evaluated using benchmark test problems "the project scheduling problem library PSPLIB [20]". Simulation results of the proposed algorithm are compared with EDA algorithm [16]. The obtained results showed that the effectiveness of the proposed algorithm and outperformed compared EDA algorithm

Social influence plays an important role in product marketing. However, rarely has it been considered in traditional recommender systems. In our work, we search to improve traditional recommender systems with utilizing information in social networks including user preferences, items' general acceptance, and influence from social friends. A user interest ontology is developed to make personalized recommendations from such information. In this paper, we present a preliminary work that sheds light on the role of social networks as sources for the development of recommendation systems. The need for user interest ontology in recommender systems and its importance as a reference to find similar items in social network is also emphasized. Finally, we describe and account for the role of user interest model based on user interest ontology to deal with the lack of semantic information in personalized recommendation system

This paper proposes a Parallel Guided Local Search (PGLS) framework for continuous optimization. In PGLS, several guided local search (GLS) procedures (agents) are run for solving the optimization problem. The agents exchange information for speeding up the search. For example, the information exchanged could be knowledge about the landscape obtained by the agents. The proposed algorithm is applied to continuous optimization problems. The preliminary experimental results show that the algorithm is very promising