Modern information technologies and the advent of machines powered by ARTIFICIAL intelligence (AI) have already strongly influenced the world of work in the 21st century. Computers, algorithms and software simplify everyday tasks, and it is impossible to imagine how most of our life could be managed without them. However, is it also impossible to imagine how most process steps could be managed without human force? The information economy characterised by exponential growth replaces the mass production industry based on economy of scales
标签： AI-and-Robotics-IBA-GEI-April 2017
上传时间： 2020-06-10
上传用户：shancjb
Forewords to books can play a variety of roles. One is to describe in more general terms what the book is about. That’s not really neces- sary, since Jim Sterne is a master at communicating complex topics in relatively simple terms.
标签： Intelligence ARTIFICIAL Marketing for
上传时间： 2020-06-10
上传用户：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.
标签： Intelligence ARTIFICIAL System Power in
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上传用户：shancjb
Inventors have long dreamed of creating machines that think. This desire dates back to at least the time of ancient Greece. The mythical figures Pygmalion, Daedalus, and Hephaestus may all be interpreted as legendary inventors, and Galatea, Talos, and Pandora may all be regarded as ARTIFICIAL life ( , Ovid and Martin 2004 Sparkes 1996 Tandy 1997 ; , ; , ).
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上传用户：shancjb
ARTIFICIAL Intelligence (AI) has undoubtedly been one of the most important buz- zwords over the past years. The goal in AI is to design algorithms that transform com- puters into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness shown by superhuman; it rather often involves very basic problems that humans solve very frequently in their day-to-day life. This can be as simple as recognizing faces in an image, driving a car, playing a board game, or reading (and understanding) an article in a newspaper. The intelligent behaviour ex- hibited by humans when “reading” is one of the main goals for a subfield of AI called Natural Language Processing (NLP). Natural language 1 is one of the most complex tools used by humans for a wide range of reasons, for instance to communicate with others, to express thoughts, feelings and ideas, to ask questions, or to give instruc- tions. Therefore, it is crucial for computers to possess the ability to use the same tool in order to effectively interact with humans.
标签： Embeddings Processing Language Natural in
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ARTIFICIAL Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. The subtitle of this book is “A Modern Approach.” The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common frame- work, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable.
标签： A-Modern-Approach Intelligence
上传时间： 2020-06-10
上传用户：shancjb
随着人类社会的进步，科学技术的发展日新月异，模拟人脑神经网络的人工神经网络已取得了长足的发展。经过半个多世纪的发展，人工神经网络在计算机科学，人工智能，智能控制等方面得到了广泛的应用。当代社会是一个讲究效率的社会，科技更新领域也是如此。在人工神经网络研究领域，算法的优化显得尤为重要，对提高网络整体性能举足轻重.BP神经网络模型是目前应用最为广泛的一种神经网络模型，对于解决非线性复杂问题具有重要的意义。但是BP神经网络有其自身的一些不足（收敛速度慢和容易陷入局部极小值问题），在解决某些现实问题的时候显得力不从心。针对这个问题，本文利用遗传算法的并行全局搜索的优势，能够弥补BP网络的不足，为解决大规模复杂问题提供了广阔的前景。本文将遗传算法与BP网络有机地结合起来，提出了一种新的网络结构，在稳定性、学习性和效率方面都有了很大的提高。基于以上的研究目的，本文首先设计了BP神经网络结构，在此基础上，应用遗传算法进行优化，达到了加快收敛速度和全局寻优的效果。本文借助MATLAB平台，对算法的优化内容进行了仿真实验，得出的效果也符合期望值，实现了对BP算法优化的目的。关键词：生物神经网络：人工神经网络；BP网络；遗传算法；仿真随着电子计算机的问世及发展，人们试图去了解人的大脑，进而构造具有人类思维的智能计算机。在具有人脑逻辑推理延伸能力的计算机战胜人类棋手的同时，引发了人们对模拟人脑信息处理的人工神经网络的研究。1.1研究背景人工神经网络（ARTIFICIAL Noural Networks，ANN）（注：简称为神经网络），是一种数学算法模型，能够对信息进行分布式处理，它模仿了动物的神经网络，是对动物神经网络的一种具体描述。这种网络依赖系统的复杂程度，通过调节内部大量节点之间的关系，最终实现信息处理的目的。人工神经网络可以通过对输入输出数据的分析学习，掌握输入与输出之间的潜在规则，能够对新数据进行分析计算，推算出输出结果，因为人工神经网络具有自适应和自学习的特性，这种学习适应的过程被称为“训练"。
上传时间： 2022-06-16
上传用户：jiabin
