Particle-swarm-OPTIMIZATION-PSO-for-MPPT-master
標(biāo)簽： SailingSim-Matlab-master
上傳時間： 2017-03-11
上傳用戶：zosoong
在微電網(wǎng)調(diào)度過程中綜合考慮經(jīng)濟、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 增強了算法的穩(wěn)定性與全局搜索能力，同時使優(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
上傳時間： 2019-11-11
上傳用戶：Dr.趙勁帥
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算法》介紹了一些基于EDA的創(chuàng)新算法。
上傳時間： 2020-05-25
上傳用戶：duwenhao
Evolutionary Computation (EC) deals with problem solving, OPTIMIZATION, and machine learning techniques inspired by principles of natural evolution and ge- netics. Just from this basic definition, it is clear that one of the main features of the research community involved in the study of its theory and in its applications is multidisciplinarity. For this reason, EC has been able to draw the attention of an ever-increasing number of researchers and practitioners in several fields.
標(biāo)簽： Applications Evolutionary Computing of
上傳時間： 2020-05-26
上傳用戶：shancjb
When joining Siemens in 2001, I also extended my research interest towards radio net- work planning methodologies. This area of research brought together my personal interest in mobile communications and in the design of efficient algorithms and data structures. Between 2001 and 2003, I participated in the EU project Momentum, which was target- ing the performance evaluation and OPTIMIZATION of UMTS radio networks. I
標(biāo)簽： Efficient Methods WCDMA for
上傳時間： 2020-05-27
上傳用戶：shancjb
The recent developments in full duplex (FD) commu- nication promise doubling the capacity of cellular networks using self interference cancellation (SIC) techniques. FD small cells with device-to-device (D2D) communication links could achieve the expected capacity of the future cellular networks (5G). In this work, we consider joint scheduling and dynamic power algorithm (DPA) for a single cell FD small cell network with D2D links (D2DLs). We formulate the optimal user selection and power control as a non-linear programming (NLP) OPTIMIZATION problem to get the optimal user scheduling and transmission power in a given TTI. Our numerical results show that using DPA gives better overall throughput performance than full power transmission algorithm (FPA). Also, simultaneous transmissions (combination of uplink (UL), downlink (DL), and D2D occur 80% of the time thereby increasing the spectral efficiency and network capacity
標(biāo)簽： Full-Duplex Cells Small
上傳時間： 2020-05-27
上傳用戶：shancjb
This paper reviews key factors to practical ESD protection design for RF and analog/mixed-signal (AMS) ICs, including general challenges emerging, ESD-RFIC interactions, RF ESD design OPTIMIZATION and prediction, RF ESD design characterization, ESD-RFIC co-design technique, etc. Practical design examples are discussed. It means to provide a systematic and practical design flow for whole-chip ESD protection design OPTIMIZATION and prediction for RF/AMS ICs to ensure 1 st Si design success.
標(biāo)簽： ESD_protection_for_RF_and_AMS_ICs
上傳時間： 2020-06-05
上傳用戶：shancjb
Why did an electricity market emerge? How does it really work? What are the perfor- mance measures that we can use to tell that the electricity market under consideration is well functioning? These are the questions that will be explored in this book. The main purpose of this book is to introduce the fundamental theories and concepts that underpintheelectricitymarketswhicharebasedonthreemajordisciplines:electrical power engineering, economics, and OPTIMIZATION methods.
標(biāo)簽： Electricity Theories Markets
上傳時間： 2020-06-07
上傳用戶：shancjb
The basic topic of this book is solving problems from system and control theory using convex OPTIMIZATION. We show that a wide variety of problems arising in system and control theory can be reduced to a handful of standard convex and quasiconvex OPTIMIZATION problems that involve matrix inequalities. For a few special cases there are “analytic solutions” to these problems, but our main point is that they can be solved numerically in all cases. These standard problems can be solved in polynomial- time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are tractable, at least in a theoretical sense. Recently developed interior-point methods for these standard problems have been found to be extremely efficient in practice. Therefore, we consider the original problems from system and control theory as solved.
標(biāo)簽： Linear_Matrix_Inequalities_in_Sys tem
上傳時間： 2020-06-10
上傳用戶：shancjb
n recent years, there have been many books published on power system OPTIMIZATION. Most of these books do not cover applications of artifi cial intelligence based methods. Moreover, with the recent increase of artifi cial intelligence applications in various fi elds, it is becoming a new trend in solving OPTIMIZATION problems in engineering in general due to its advantages of being simple and effi cient in tackling complex problems. For this reason, the application of artifi cial intelligence in power systems has attracted the interest of many researchers around the world during the last two decades. This book is a result of our effort to provide information on the latest applications of artifi cial intelligence to OPTIMIZATION problems in power systems before and after deregulation.
標(biāo)簽： Intelligence Artificial System Power in
上傳時間： 2020-06-10
上傳用戶：shancjb
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