Search - Artificial Intelligence

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[Other] yichuansuanfa--lilunyingyongyuruanjianshixian DL : 0: 遗传算法是一种借鉴生物界自然选择和进化机制发展起来的高度并行、随机、自适应搜索算法。由于其具有健壮性，特别适合于处理传统搜索算法解决不好的复杂的和非线性问题。以遗传算法为核心的进化算法已与模糊系统理论、人工神经网络等一起成为计算智能研究中的热点，受到许多学科的共同关注。本书全面系统地介绍了遗传算法的基本理论，重点介绍了遗传算法的经典应用和国内外的新-Genetic Algorithm is a kind of drawing on biological mechanisms of natural selection and evolutionary development of highly parallel, randomized, adaptive search algorithm. Due to its robustness, particularly suited to deal with traditional search algorithms are not properly solved complex and nonlinear problems. To genetic algorithms as the core of the evolutionary algorithm with fuzzy system theory, artificial neural networks, along with computational intelligence research hotspot by many subjects of common concern. This book comprehensively and systematically introduce the genetic algorithm
Date : Size : 5.94mb User : 涂满园
[Other] AdaptiveStepsizeEASIAlgorithmBas DL : 0: 粒子群优化算法是一类基于群智能的随机优化算法。因受到人工生命的研究结果启发， &’((’)* 和 +,’-./-0 1 ，!2 于 334 年提出了粒子群优化算法，并已广泛应用于函数优化，神经网络训练，模式分类、模糊系统控制以及其他的应用领域。-PSO is a kind of swarm intelligence-based stochastic optimization algorithms. Inspired by the study results due to artificial life, & ' ((' )* and+, ' -./-0 1 ,! 2 334 made in PSO, and has been widely used in function optimization, neural network training, pattern classification, fuzzy systems control and other applications.
Date : Size : 866kb User : kobe
[Other] bimpb DL : 0: MIT Artificial Intelligence Laboratory identification of the target source, Achieve canonical correlation analysis, The IMC - PID is using the internal model control principle for PID parameters is calculated.
Date : Size : 8kb User : feijenhou
[Other] 第198期（20171028）：ITI：人工智能政策准则 DL : 0: 关于人工智能的政策；让初学者不迷茫，找到重点(Policy on artificial intelligence; let beginners not be confused and find the key)
Date : Size : 285kb User : 安琪是个男孩
[Other] Deep_Learning_for_Radar DL : 0: Deep Learning for Radar and Communications Automatic Target Recognition presents a comprehensive illustration of modern artificial intelligence/machine learning (AI/ML) technology for radio frequency (RF) data exploitation. While numerous textbooks focus on AI/ML technology for non-RF data such as video images, audio speech, and spoken text, there is no such book for data in the RF spectrum. Hence, there is a need for an RF machine learning (ML) book for the research community that captures state-of-the-art AI/ML and deep learning (DL) algorithms and future challenges. Our goals with this book are to provide the practitioner with (i) an overview of the important ML/DL techniques, (ii) an exposition of the technical challenges associated with developing ML methods for RF applications, and (iii) implementation of ML techniques on synthetic aperture radar (SAR) imagery and communication signals classification.
Date : 2023-03-23 Size : 7.47mb User : sadovski
[Other] Understanding deep learning DL : 0: Artificial intelligence (AI) is concerned with building systems that simulate intelligent behavior. It encompasses a wide range of approaches, including those based on logic, search, and probabilistic reasoning. Machine learning is a subset of AI that learns to make decisions by fitting mathematical models to observed data. This area has seen explosive growth and is now (incorrectly) almost synonymous with the term AI. A deep neural network is one type of machine learning model, and when this model is fitted to data, this is referred to as deep learning. At the time of writing, deep networks are the most powerful and practical machine learning models and are often encountered in day-to-day life. It is commonplace to translate text from another language using a natural language processing algorithm, to search the internet for images of a particular object using a computer vision system, or to converse with a digital assistant via a speech recognition interface. All of these applications are powered by deep learning. As the title suggests, this book aims to help a reader new to this field understand the principles behind deep learning. The book is neither terribly theoretical (there are no proofs) nor extremely practical (there is almost no code). The goal is to explain the underlying ideas; after consuming this volume, the reader will be able to apply deep learning to novel situations where there is no existing recipe for success. Machine learning methods can coarsely be divided into three areas: supervised, unsupervised, and reinforcement learning. At the time of writing, the cutting-edge methods in all three areas rely on deep learning (figure 1.1). This introductory chapter describes these three areas at a high level, and this taxonomy is also loosely reflected in the book’s organization.
Date : 2023-07-03 Size : 11.11mb User : ihaveap1

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