外文翻译神经网络概述.docx

1、外文翻译神经网络概述外文原文与译文 外文原文Neural NetworkIntroduction1.ObjectivesAs you read these words you are using a complex biological neural network. You have a highly interconnected set of some 1011 neurons to facilitate your reading, breathing, motion and thinking. Each of your biological neurons，a rich assembly

2、 of tissue and chemistry, has the complexity, if not the speed, of a microprocessor. Some of your neural structure was with you at birth. Other parts have been established by experience.Scientists have only just begun to understand how biological neural networks operate. It is generally understood t

3、hat all biological neural functions, including memory, are stored in the neurons and in the connections between them. Learning is viewed as the establishment of new connections between neurons or the modification of existing connections.This leads to the following question: Although we have only a r

4、udimentary understanding of biological neural networks, is it possible to construct a small set of simple artificial “neurons” and perhaps train them to serve a useful function? The answer is “yes.”This book, then, is about artificial neural networks.The neurons that we consider here are not biologi

5、cal. They are extremely simple abstractions of biological neurons, realized as elements in a program or perhaps as circuits made of silicon. Networks of these artificial neurons do not have a fraction of the power of the human brain, but they can be trained to perform useful functions. This book is

6、about such neurons, the networks that contain them and their training.2.HistoryThe history of artificial neural networks is filled with colorful, creative individuals from many different fields, many of whom struggled for decades to develop concepts that we now take for granted. This history has bee

7、n documented by various authors. One particularly interesting book is Neurocomputing: Foundations of Research by John Anderson and Edward Rosenfeld. They have collected and edited a set of some 43 papers of special historical interest. Each paper is preceded by an introduction that puts the paper in

8、 historical perspective.Histories of some of the main neural network contributors are included at the beginning of various chapters throughout this text and will not be repeated here. However, it seems appropriate to give a brief overview, a sample of the major developments.At least two ingredients

9、are necessary for the advancement of a technology: concept and implementation. First, one must have a concept, a way of thinking about a topic, some view of it that gives clarity not there before. This may involve a simple idea, or it may be more specific and include a mathematical description. To i

10、llustrate this point, consider the history of the heart. It was thought to be, at various times, the center of the soul or a source of heat. In the 17th century medical practitioners finally began to view the heart as a pump, and they designed experiments to study its pumping action. These experimen

11、ts revolutionized our view of the circulatory system. Without the pump concept, an understanding of the heart was out of grasp.Concepts and their accompanying mathematics are not sufficient for a technology to mature unless there is some way to implement the system. For instance, the mathematics nec

12、essary for the reconstruction of images from computer-aided topography (CAT) scans was known many years before the availability of high-speed computers and efficient algorithms finally made it practical to implement a useful CAT system.The history of neural networks has progressed through both conce

13、ptual innovations and implementation developments. These advancements, however, seem to have occurred in fits and starts rather than by steady evolution.Some of the background work for the field of neural networks occurred in the late 19th and early 20th centuries. This consisted primarily of interd

14、isciplinary work in physics, psychology and neurophysiology by such scientists as Hermann von Helmholtz, Ernst Much and Ivan Pavlov. This early work emphasized general theories of learning, vision, conditioning, etc.,and did not include specific mathematical models of neuron operation.The modern vie

15、w of neural networks began in the 1940s with the work of Warren McCulloch and Walter Pitts McPi43, who showed that networks of artificial neurons could, in principle, compute any arithmetic or logical function. Their work is often acknowledged as the origin of theneural network field.McCulloch and P

16、itts were followed by Donald Hebb Hebb49, who proposed that classical conditioning (as discovered by Pavlov) is present because of the properties of individual neurons. He proposed a mechanism for learning in biological neurons.The first practical application of artificial neural networks came in th

17、e late 1950s, with the invention of the perception network and associated learning rule by Frank Rosenblatt Rose58. Rosenblatt and his colleagues built a perception network and demonstrated its ability to perform pattern recognition. This early success generated a great deal of interest in neural ne

18、twork research. Unfortunately, it was later shown that the basic perception network could solve only a limited class of problems. (See Chapter 4 for more on Rosenblatt and the perception learning rule.)At about the same time, Bernard Widrow and Ted Hoff WiHo60 introduced a new learning algorithm and

19、 used it to train adaptive linear neural networks, which were similar in structure and capability to Rosenblatts perception. The Widrow Hoff learning rule is still in use today. (See Chapter 10 for more on Widrow-Hoff learning.)Unfortunately, both Rosenblatts and Widrows networks suffered from the s

20、ame inherent limitations, which were widely publicized in a book by Marvin Minsky and Seymour Papert MiPa69. Rosenblatt and Widrow wereaware of these limitations and proposed new networks that would overcome them. However, they were not able to successfully modify their learning algorithms to train

21、the more complex networks.Many people, influenced by Minsky and Papert, believed that further research on neural networks was a dead end. This, combined with the fact that there were no powerful digital computers on which to experiment,caused many researchers to leave the field. For a decade neural

22、network research was largely suspended. Some important work, however, did continue during the 1970s. In 1972 Teuvo Kohonen Koho72 and James Anderson Ande72 independently and separately developed new neural networks that could act as memories. Stephen Grossberg Gros76 was also very active during this

23、 period in the investigation of self-organizing networks.Interest in neural networks had faltered during the late 1960s because of the lack of new ideas and powerful computers with which to experiment. During the 1980s both of these impediments were overcome, and researchin neural networks increased

24、 dramatically. New personal computers andworkstations, which rapidly grew in capability, became widely available. In addition, important new concepts were introduced. Two new concepts were most responsible for the rebirth of neural net works. The first was the use of statistical mechanics to explain

25、 the operation of a certain class of recurrent network, which could be used as an associative memory. This was described in a seminal paper by physicist John Hopfield Hopf82. The second key development of the 1980s was the backpropagation algo rithm for training multilayer perceptron networks, which

26、 was discovered independently by several different researchers. The most influential publication of the backpropagation algorithm was by David Rumelhart and James McClelland RuMc86. This algorithm was the answer to the criticisms Minsky and Papert had made in the 1960s. (See Chapters 11 and 12 for a

27、 development of the backpropagation algorithm.)These new developments reinvigorated the field of neural networks. In the last ten years, thousands of papers have been written, and neural networks have found many applications. The field is buzzing with new theoretical and practical work. As noted bel

28、ow, it is not clear where all of this will lead US.The brief historical account given above is not intended to identify all of the major contributors, but is simply to give the reader some feel for how knowledge in the neural network field has progressed. As one might note, the progress has not alwa

29、ys been slow but sure. There have been periods of dramatic progress and periods when relatively little has been accomplished.Many of the advances in neural networks have had to do with new concepts, such as innovative architectures and training. Just as important has been the availability of powerfu

30、l new computers on which to test these new concepts. Well, so much for the history of neural networks to this date. The real question is, What will happen in the next ten to twenty years? Will neural networks take a permanent place as a mathematical/engineering tool, or will they fade away as have s

31、o many promising technologies? At present, the answer seems to be that neural networks will not only have their day but will have a permanent place, not as a solution to every problem, but as a tool to be used in appropriate situations. In addition, remember that we still know very little about how

32、the brain works. The most important advances in neural networks almost certainly lie in the future.Although it is difficult to predict the future success of neural networks, the large number and wide variety of applications of this new technology are very encouraging. The next section describes some of these applications.3.ApplicationsA recent newspaper article described the use of neural networks in literature research by Aston University. It stated that the network can be taught to recognize individual writing styles, and the researchers used it to