Search results for "algorithm."

Multilayer neural networks: an experimental evaluation of on-line training methods

2004

Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…

Learning the structure of HMM's through grammatical inference techniques

2002

A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >

Transaction Costs and Returns to a Trading Strategy

2017

This chapter starts with a review of transaction costs in capital markets. Then it demonstrates how to simulate the returns to a moving average trading strategy in the presence of transaction costs. The following two cases are considered when a trading indicator generates a sell signal: case one where the trader switches to cash, and case two where the trader alternatively sells short a financial asset.

Genome-wide analysis of factors regulating gene expression in liver

2007

In recent decades, multiple individual genes have been studied with respect to their level of expression in liver tissue and in many cases substantial progress has been made in identifying individual factors promoting gene expression in liver. However, the overall picture is still undefined and general rules or factors regulating gene expression in liver have not yet been established. Thus, a genome-wide screen for factors regulating gene expression in liver is of high interest, as it may reveal common regulatory mechanisms for most genes highly expressed in liver. These factors represent potential new targets in liver disease associated with differential gene expression. Using a novel bioi…

Tame dynamics and robust transitivity chain-recurrence classes versus homoclinic classes

2014

Performance analysis of amplify-and-forward cooperative communication systems with channel estimation errors

2008

Cooperative diversity is a transmission technique that achieves a diversity gain by using a combination of the relayed signal and the direct signal. In this paper, we study the symbol-error-rate (SER) performance of a cooperative communication system operating in an amplify-and-forward (AF) mode, where the channel state information (CSI) available at the receiver is an estimate of the channel gains with estimation errors. We derive both the probability density function and the moment generating function of the instantaneous signal-to-noise ratio (SNR) at the destination terminal. These statistical quantities are then applied to study the performance of an AF cooperative communication system…

A multi-objective genetic algorithm for the passenger maritime transportation problem

2014

Over the last years, the transportation demand has continuously increased and a further growth is predicted for the next future especially as regards the maritime sector. As a consequence, shipping companies will be asked to improve the supplied services in order to assure a high quality and time-effective goods and passengers transportation, deriving at the same time their own benefits by minimizing costs. Therefore, the optimization of routes and schedules together with the fleet deployment take a meaningful role on companies profitability and efficiency. In such a perspective, the present paper proposes a multi-objective mathematical programming model to determine a set of routes and sch…

A branch-price-and-cut algorithm for the capacitated multiple vehicle traveling purchaser problem with unitary demand

2021

Abstract The multiple vehicle traveling purchaser problem (MVTPP) consists of simultaneously selecting suppliers and routing a fleet of homogeneous vehicles to purchase different products at the selected suppliers so that all product demands are fulfilled and traveling and purchasing costs are minimized. We consider variants of the MVTPP in which the capacity of the vehicles can become binding and the demand for each product is one unit. Corresponding solution algorithms from the literature are either branch-and-cut or branch-and-price algorithms, where in the latter case the route-generation subproblem is solved on an expanded graph by applying standard dynamic-programming techniques. Our …

Improved heuristics for the regenerator location problem

2014

Telecommunication systems use optical signals to transmit information. The strength of a signal in an optical network deteriorates and loses power as it goes farther from the source, mainly due to attenuation. Therefore, to enable the signal to arrive its intended destination with good quality, it is necessary to regenerate the signal periodically using regenerators. These components are relatively expensive and therefore it is desirable to deploy as few of them as possible in the network. In the regenerator location problem (RLP), we are given an undirected graph, positive edge lengths, and a parameter specifying the maximum length that a signal can travel before its quality deteriorates a…

Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

2018

Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …