Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.     

岩爆预测的最大熵最优相对隶属度方法

通过分析国内外岩爆预测的判据,选择岩爆发生所需的力学条件、完整性条件、储能条件和脆性条件作为岩爆预测指标.引入岩爆预测的相对隶属度概念,计算了岩爆的相对隶属度模糊矩阵和预测指标的权重,以信息熵来描述并比较岩爆评价中的不确定性,定义了加权广义权距离来表征岩爆的差异.根据最大熵原理建立了岩爆预测的模糊最优化模型,对一些岩石...     

THE CONCEPT OF ASPHALTENES REVISITED

ABSTRACT

Petroleum asphaltenes are defined as material insoluble in pentane or heptane and the nature of the molecular species that constitute asphaltenes has been a well researched subject over the past three decades. These investigations have led to a general consensus of the majority of researchers that asphaltenes contain condensed aromatic systems carrying alkyl, cycloalkyl, and heteroatom substituents. A variety of molecular models has been developed which have been used to explain the behavior of asphaltenes in feedstocks during recovery and processing operations

However, an alternate concept introduced the idea that asphaltenes can be defined as a complex mixture of organic compounds of varying molecular weight and polarity. This allows asphaltenes to be described more fully in terms of their behavior during processes and properties in products, such as asphalt

Thus, the concept can be used to explain, even predict, the incompatibility of asphaltenes with the other petroleum constituents as well as the deposition of asphaltic material on reservoir rock and the occurrence of sediments during refinery operations.     

On the notion of concept II

In this paper, we carry on with the analysis of constructible concepts initiated in [M. Freund, On the notion of concept 1. Artificial Intelligence 172 (2008) 570-590], and examine the key notions of categorization theory that are linked with category-based induction. In the main part of the article, we propose a reformulation of classical prototype theory using the framework of monotonic and non-monotonic logics. In this perspective, the classical notions of essence and intension are respectively interpreted as sets of necessary and defeasible consequences, giving rise to a relation ? analogous to that of classical consequence, and a relation ∼ which behaves, relatively to ?, like a supraclassical rational inference relation. This formal analogy between the language of categorization theory and that of propositional logic reveals itself to be particularly useful when dealing with the problem of category-based induction.     

图象平滑与边缘检测的模糊向量描述

本文对噪声和边缘的统计特征进行了分析，提出了以Ｐ１（ｉ，ｊ），Ｐ２（ｉ，ｊ）和Ｐ３（ｉ，ｊ）作为边缘、噪声和非边缘非噪声的尿属函数，并在此基础上建立它们的特征描述向量。借助于该特征向量，将图像平滑和边缘检测转化为一个模式识别的问题，从理论上讨论了解决图像处理中长期存在的去噪和保持边缘细节的折衷问题的思路，并构造了基于模糊模式识别的图像边缘检测和滤波的基本框架。     

Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory

Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible.In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).     

Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system

The main objective of this paper is to solve the bi-objective reliability redundancy allocation problem for series-parallel system where reliability of the system and the corresponding designing cost are considered as two different objectives. In their formulation, reliability of each component is considered as a triangular fuzzy number. In order to solve the problem, developed fuzzy model is converted to a crisp model by using expected values of fuzzy numbers and taking into account the preference of decision maker regarding cost and reliability goals. Finally the obtained crisp optimization problem has been solved with particle swarm optimization (PSO) and compared their results with genetic algorithm (GA). Examples are shown to illustrate the method. Finally statistical simulation has been performed for supremacy the approach.     

New members and associates of the American Psychological Association.

The Board of Directors announces that the following 2,001 persons were elected to initial membership in the American Psychological Association by the Membership Committee, effective as of July 1, 1987. Of these, 1,630 were elected as Members, and 371 were elected as Associate members. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种模糊自适应BP算法的研究

本文在人工神经网络中引入模糊集理论，提出一种模糊自适应ＢＰ算法。通过奇偶校验和ＥＥＧ异常波形检测两上实例，验证了新算法在学习速度与性能上都优于传统的ＢＰ和虎法。