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This research paper investigates a target extraction method of infrared point targets and proposes a type of space-time combined target detection algorithm. Starting from the detected target information which has a low signal-to-noise ratio and with very few target characteristics from a small target infrared image formation, it uses an algorithm for least squares involution filtering of the space...
In today's fiercely competing market environment, a growing number of companies have begun to realize the importance of implementing an integrated logistics management. Thus, hire party logistics service providers (3PLs) must improve their logistics networks to support such integrated supply chain management incorporating forward and reverse logistics flows. This paper presents a mixed integer nonlinear...
In multi-objective particle swarm optimization (MOPSO) methods, selecting good local guides (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. This paper introduces the Particle angle division method as a new method for finding the global best particle for each particle of the population...
In order to avoid the shortage of intensity threshold, we propose an edge detection approach based on an instinct behavior orientation-based similarity of edge(OBS), which is absolutely independent from intensity gradient. We first form series of overlapping curves by using OBS and then identify the pixels in the majority rule. A system designed by using some immunological principles is the executor...
Formal sufficient and necessary condition for the deterministic standard PSO algorithm to converge to equilibrium point, diverge to infinity or oscillate within a range is derived based on the discrete time dynamic system theory. General guidelines for parameters selection are provided according to the theory analysis. It is pointed out that, strictly speaking, the currently popular view that small...
One of the most fundamental problems in the practice of Genetic Algorithms (GAs) is the choice of population size N. Theoretical investigation of this problem with a finite population size requires stochastic theory. In this study, We examined effects of stochastic fluctuation in a GA on the multiplicative landscape. We used Markov chain model and its diffusion approximation to calculate the distribution...
As traditional network intrusion detection based on pattern recognition can just get better classification accurately only with lots of prior knowledge which is difficult to obtain, a novel ensemble learning algorithm for fuzzy classification rules is presented. The fuzzy antecedent is adjusted based on the combination of ensemble learning and induction-enhanced particle swarm optimization for intrusion...
We present a general approach to speeding up a family of multi-relational data mining algorithms that using ID propagation to obtain the information needed for building decision tree classifier from relational database. Preliminary results of our experiments suggest that the proposed method can yield 1-2 orders of magnitude reductions in the running time of such algorithms without any deterioration...
Ensemble of classifiers is a learning paradigm where a set of classifiers are jointly used to improve the classification accuracy. The main contributions of this paper include: (1) proposing a new concept named attribute selection set based on Gene Expression Programming (GEP), (2) analyzing the principle of classifier ensemble, (3) proposing an Attribute- Oriented Ensemble Classifier Based on Niche...
XCSFG is a new version of XCSF with the ability of computing the environmental payoff using Genetic Algorithm. In the first version of XCSFG, this computation was done by evolving coefficients of the associated linear payoff functions. In this paper, we extend XCSFG to approximate the payoff functions in the form of higher order polynomials. Our newly proposed method uses GA with variable-length chromosome...
In many applications, there are problems of small sample size and high dimensionality of data, for example, in traditional Chinese medicine syndrome classification of chronic gastritis. To attack these problems, this paper gives a method which combines data preprocessing and Bayesian networks. Firstly, data is divided into groups with hierarchical clustering. Then, principal component analysis technique...
Feature extraction is significant for pattern analysis and classification. Those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. Most recently, Zhao et al. presented a direct evolutionary feature extraction algorithm(DEFE) which can reduce the space complexity and improve the efficiency,...
Function Discovery is an important research direction in Data Mining and Economic Statistical Target Forecast. Gene Expression Programming (GEP) is a new tool to discovery the function in economic target analysis field. To overcome the deficiency such as pre-maturity and biggish stagnancy generation in GEP, this study (1) Introduces a dynamic mutation operator ( DM-GEP ) and flexibility controlling...
Nonlinear dimensionality reduction from points underlying low dimension manifolds with outliers in high dimensional space is a challenge problem. In this paper, we proposed a robust dimensionality reduction method which can learn the low dimensional embeddings of manifold from input high dimensional data with large percentage of outliers. The proposed method named tensor tangent space alignment operates...
Negative selection algorithm (NSA) lacks adaptability and needs a large number of self elements to build the profile of the system and train detectors. In order to overcome these limitations and build an appropriate profile of the system in a varying self and nonself condition, this paper presents a feedback negative selection algorithm, which is referred to FNSA algorithm, and its applications to...
This paper describes research that was performed to verify the possibility of learning an economical time series using a neural network. An actual economical time series wasn't used for this research, but, instead, verification was performed using data obtained from an equation developed in the field of econophysics that simulates an economical time series. This data changes irregularly, and it is...
Web services are gaining acceptance as a standards-based approach for integrating loosely coupled services. Achieving high levels of reliability and availability of service-oriented application server in spite of service or infrastructure failures poses new challenges. According to the characteristic of performance parameters of service-oriented application sever, a new software aging forecasting...
Grid Infrastructures have been used to solve large scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. Predicting the runtime of a task, an important component of the resource management, plays an important role in the task scheduling and the resource using in computational...
In information system, some rules have implicating relations (called implicating rules), but some rules have not. An approach of finding implicating rules based on the genetic algorithm is proposed. Some properties of independence and correlation of descriptions are discussed. It can obtain directly the implicating rules (including the positive and negative rules) with the correlation of two descriptions...
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