The Fuzzy Logic Toolbox™ product extends the MATLAB® technical computing environment with tools for designing systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy CLustering and adaptive neurofuzzy learning.
標(biāo)簽： environment computing technical Toolbox
上傳時(shí)間： 2013-12-29
上傳用戶：fandeshun
Quartz is a full-featured, open source job scheduling system that can be integrated with, or used along side virtually any J2EE or J2SE application - from the smallest stand-alone application to the largest e-commerce system. Quartz can be used to create simple or complex schedules for executing tens, hundreds, or even tens-of-thousands of jobs jobs whose tasks are defined as standard Java components or EJBs. The Quartz Scheduler includes many enterprise-class features, such as JTA transactions and CLustering. Quartz is freely usable, licensed under the Apache 2.0 license.
標(biāo)簽： full-featured integrated scheduling Quartz
上傳時(shí)間： 2017-08-07
上傳用戶：來茴
LEACH CLustering algorithm matlab code, detailed notes for the students of great help beginners WSN protocol
標(biāo)簽： LEACH
上傳時(shí)間： 2016-01-07
上傳用戶：huoniao741
在微電網(wǎng)調(diào)度過程中綜合考慮經(jīng)濟(jì)、環(huán)境、蓄電池的 循環(huán)電量，建立多目標(biāo)優(yōu)化數(shù)學(xué)模型。針對傳統(tǒng)多目標(biāo)粒子 群算法(multi-objective particle swarm optimization，MOPSO) 的不足，提出引入模糊聚類分析的多目標(biāo)粒子群算法 (multi-objective particle swarm optimization algorithm based on fuzzy CLustering，F(xiàn)CMOPSO)，在迭代過程中引入模糊聚 類分析來尋找每代的集群最優(yōu)解。與 MOPSO 相比， FCMOPSO 增強(qiáng)了算法的穩(wěn)定性與全局搜索能力，同時(shí)使優(yōu) 化結(jié)果中 Pareto 前沿分布更均勻。在求得 Pareto 最優(yōu)解集 后，再根據(jù)各目標(biāo)的重要程度，用模糊模型識別從最優(yōu)解集 中找出不同情況下的最優(yōu)方案。最后以一歐洲典型微電網(wǎng)為 例，驗(yàn)證算法的有效性和可行性。
標(biāo)簽： 模糊 模型識別 微電網(wǎng) 多目標(biāo)優(yōu)化 聚類分析
上傳時(shí)間： 2019-11-11
上傳用戶：Dr.趙勁帥
Smart Grids provide many benefits for society. Reliability, observability across the energy distribution system and the exchange of information between devices are just some of the features that make Smart Grids so attractive. One of the main products of a Smart Grid is to data. The amount of data available nowadays increases fast and carries several kinds of information. Smart metres allow engineers to perform multiple measurements and analyse such data. For example, information about consumption, power quality and digital protection, among others, can be extracted. However, the main challenge in extracting information from data arises from the data quality. In fact, many sectors of the society can benefit from such data. Hence, this information needs to be properly stored and readily available. In this chapter, we will address the main concepts involving Technology Information, Data Mining, Big Data and CLustering for deploying information on Smart Grids.
標(biāo)簽： Processing Cities Smart Data in
上傳時(shí)間： 2020-05-23
上傳用戶：shancjb
Smart Grids provide many benefits for society. Reliability, observability across the energy distribution system and the exchange of information between devices are just some of the features that make Smart Grids so attractive. One of the main products of a Smart Grid is to data. The amount of data available nowadays increases fast and carries several kinds of information. Smart metres allow engineers to perform multiple measurements and analyse such data. For example, information about consumption, power quality and digital protection, among others, can be extracted. However, the main challenge in extracting information from data arises from the data quality. In fact, many sectors of the society can benefit from such data. Hence, this information needs to be properly stored and readily available. In this chapter, we will address the main concepts involving Technology Information, Data Mining, Big Data and CLustering for deploying information on Smart Grids.
標(biāo)簽： Processing Cities Smart Data
上傳時(shí)間： 2020-05-25
上傳用戶：shancjb
壓縮包中有5篇論文，分別為《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》這是一篇博士論文，較為詳細(xì)的介紹了各種EDA算法；《Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive CLustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二階混合矩的高斯分布估計(jì)算法》介紹了一些基于EDA的創(chuàng)新算法。
標(biāo)簽： EDA 分布估計(jì)算法 論文
上傳時(shí)間： 2020-05-25
上傳用戶：duwenhao
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