Neural-Network-Introduction神经网络介绍大学毕业论文外文文献翻译及原文

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　　文献、资料中文题目：神经网络介绍

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　　翻译日期： 2017.02.14

　　外文文献翻译

　　注：节选自Neural Network Introduction神经网络介绍，绪论。

　　History

　　The 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 been 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 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 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 illustrate 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 experiments 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 necessary 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 conceptual 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 interdisciplinary 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 view 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 the

　　neural network field.

　　McCulloch and Pitts 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 the 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 network 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 used it to train adaptive linear neural networks, which were

　　similar in structure and capability to Rosenblatt’s perception. The Widrow Hoff learning rule is still in use today. (See Chapter 10 for more on Widrow-Hoff learning.)

　　Unfortunately, both Rosenblatt's and Widrow's networks suffered from the same inherent limitations, which were widely publicized in a book by Marvin Minsky and Seymour Papert [MiPa69]. Rosenblatt and Widrow were

　　aware of these limitations and proposed new networks that would overcome them.

　　However, they were not able to successfully modify their learning algorithms to train 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 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 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 research

　　in neural networks increased dramatically. New personal computers and

　　workstations, 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 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 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 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 below, 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 always 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

　　powerful 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 so

　　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 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.

　　Applications

　　A 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 compare works attributed to

　　Shakespeare and his contemporaries. A popular science television program recently

　　documented the use of neural networks by an Italian research institute to test the purity of

　　olive oil. These examples are indicative of the broad range of applications that can be

　　found for neural networks. The applications are expanding because neural networks are

　　good at solving problems, not just in engineering, science and mathematics, but m

　　medicine, business, finance and literature as well. Their application to a wide variety of

　　problems in many fields makes them very attractive. Also, faster computers and faster

　　algorithms have made it possible to use neural networks to solve complex industrial

　　problems that formerly required too much computation.

　　The following note and Table of Neural Network Applications are reproduced here from the Neural Network Toolbox for MATLAB with the permission of the Math Works, Inc.

　　The 1988 DARPA Neural Network Study [DARP88] lists various neural network applications, beginning with the adaptive channel equalizer in about 1984. This device, which is an outstanding commercial success, is a single-neuron network used in long distance telephone systems to stabilize voice signals. The DARPA report goes on to list other commercial applications, including a small word recognizer, a process monitor, a sonar classifier and a risk analysis system.

　　Neural networks have been applied in many fields since the DARPA report was written. A list of some applications mentioned in the literature follows.

　　Aerospace

　　High performance aircraft autopilots, flight path simulations, aircraft control systems, autopilot enhancements, aircraft component simulations, aircraft component fault detectors

　　Automotive

　　Automobile automatic guidance systems, warranty activity analyzers

　　Banking

　　Check and other document readers, credit application evaluators

　　Defense

　　Weapon steering, target tracking, object discrimination, facial recognition, new kinds of sensors, sonar, radar and image signal processing including data compression, feature extraction and noise suppression, signal/image identification

　　Electronics

　　Code sequence prediction, integrated circuit chip layout, process control, chip failure analysis, machine vision, voice synthesis, nonlinear modeling

　　Entertainment

　　Animation, special effects, market forecasting