基于模糊理论的非处方药的满意度评价系统

为了帮助顾客从众多对症的非处方药中选出最满意的药品,提出了开发1个基于Web的非处方药满意度评价系统。系统根据顾客提供的典型症状,采用疾病与症状相结合的选药策略,查找出所有对症的非处方药,并以药品安全性、疗效、稳定性、易用性、价格作为评价因素,根据顾客的关注角度和关注程度确定各因素权重,用模型b=W·R对对症药品进行满意度评价,按满意度高低依次推荐。经实际应用,系统推荐准确度在70%以上。     

动态描述逻辑的可拓集合扩展

通过对可拓集合与经典集合、模糊集合的分析比较,说明可拓集合的特点和优点,用可拓集合代替经典集合或模糊集合作为动态描述逻辑DDL的集合论基础,对DDL进行扩展,生成了一种新的描述逻辑DDES,并给出了DDLES中概念、关系和实例的描述形式以及它们的语义解释,最后对传统描述逻辑ALC、模糊DDL和DDES中的Abox形式进行了比较.     

基于T-S模型的输出反馈滑模控制及应用

为了使系统满足品质要求并对不确定因素具有较强的鲁棒性,采用滑模控制方法。建立被控对象T-S模糊模型。针对系统状态不完全可测特点,采用迭代线性矩阵不等式方法,利用输出信息构造滑模面。根据系统品质要求,采用参数鲁棒设计方法,配置所需极点,确定相应在参数空间的映射关系。应用平行分布补偿算法,根据映射产生的参数样本训练T-S模糊控制器,实现等效控制律。在此基础上应用高增益方法设计切换控制律,保证系统对满足匹配条件的不确定因素具有较理想的自适应性。最后,直升机模型的仿真结果表明,方法具有很好的控制效果。     

基于免疫和模糊模式识别的检测器生成模型

分析了在检测器生成过程中引入模糊模式识别的原因,提出了一种基于免疫原理和模糊模式识别的检测器生成模型,该模型的核心算法是基于隶属度的检测器选择算法.详细描述了该算法的算法流程和检测器对非我抗原集合隶属度的计算算法,并简述了初始检测器生成算法和分层检测策略.设计了仿真试验,并将实验结果同传统方法进行了比较,详述了仿真实验中各个参数选择的依据和理由,试验结果表明新模型呈现较高的检测率和低的误检率.     

基于主从粒子群的模糊小波神经网络交通控制

城市交通智能控制是ITS的重要组成部分,交叉口是决定道路通行顺畅的制的基础.为提高路口的通行能力,提出了主从结构的粒子群算法优化模糊小波神经网络参数,并将其应用于交通信号的控制.算法中,主级粒子进行全局搜索,从级粒子以主级粒子找到的最优解为中心进行局部搜索.仿真结果表明该算法能够有效减少交叉口车辆平均延误时间,提高道路通行能力.     

MIMO系统非线性模糊免疫PID的设计与优化

针对工业中常规控制器难以达到较好控制效果的控制难点,提出了免疫PID控制器,利用模糊控制可以无限逼近非线性的特性,构成非线性模糊免疫PID控制器,并基于正交函数将M1MO(多输入多输出)控制系统的非线性模糊免疫PID控制器的设计过程转化为由一个只含有代数方程的算法来实现,最后用具有自适应功能的遗传算法对其进行优化.仿真结果表明,所设计的控制器在控制性能上优于常规PID,并能快速得到最优参数.另方面对于多输入多输出控制系统,控制器的设计和优化显得更为简便和有效.     

Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation

Genetic Algorithms (GAs) and other Evolutionary Algorithms (EAs), as powerful and broadly applicable stochastic search and optimization techniques have been successfully applied in the area of management science, operations research and industrial engineering. In the past few years, researchers gave lots of great idea for improvement of evolutionary algorithms, which include population initialization, individual selection, evolution, parameter setting, hybrid approach with conventional heuristics etc. However, though lots of different versions of evolutionary computations have been created, all of them have turned most of its attention to the development of search abilities of approaches. In this paper, for improving the search ability, we focus on how to take a balance between exploration and exploitation of the search space. It is also very difficult to solve problem, because the balance between exploration and exploitation is depending on the characteristic of different problems. The balance also should be changed dynamically depend on the status of evolution process. Purpose of this paper is the design of an effective approach which it can correspond to most optimization problems. In this paper, we propose an auto-tuning strategy by using fuzzy logic control. The main idea is adaptively regulation for taking the balance among the stochastic search and local search probabilities based on the change of the average fitness of parents and offspring which is occurred at each generation. In addition, numerical analyses of different type optimization problems show that the proposed approach has higher search capability that improve quality of solution and enhanced rate of convergence.     

Some notes on Zadeh’s extensions

This paper shows that the Zadeh’s extensions of sendograph-metric-continuous fuzzy-valued functions are sendograph-metric-continuous fuzzy functions.     

Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.     

A fuzzy-based interleaved multi-hop authentication scheme in wireless sensor networks

In sensor networks, a compromised node can either generate fabricated reports with false votes or inject false votes into real reports, which causes severe damage such as false alarms, energy drain and information loss. An interleaved hop-by-hop authentication (IHA) scheme addresses the former attack by detecting and filtering false reports in a deterministic and hop-by-hop fashion. Unfortunately, in IHA, all en-route nodes must join to verify reports while only a few are necessary to the authentication procedure. In this paper, we propose a fuzzy-based interleaved multi-hop authentication scheme based on IHA. In our scheme, the fuzzy logic system only selects some nodes for verification based on the network characteristics. Moreover, we apply a voting method and a hash-based key assignment mechanism to improve network security. Through performance evaluation, the proposed scheme is found to save up to 13% of the energy consumption and to provide more network protection compared to IHA.