人工神经网络外文翻译

附录二英文参考文献原文Artificial Neural NetworksArtificial Neural Networks - Basic FeaturesComposed of a large number of processing units connected by a nonlinear, adaptive information processing system. It is the basis for modern neuroscience research findings presented, trying to simulate a large neural network processing, memory, information processing way of information. Artificial neural network has four basic characteristics:(1) non-linear non-linear relationship is the general characteristics of the natural world. The wisdom of the brain is a nonlinear phenomenon. Artificial neural activation or inhibition in two different states, this behavior mathematically expressed as a linear relationship. Threshold neurons have a network with better performance, can improve fault tolerance and storage capacity.(2) non-limitation of a neural network is usually more extensive neuronal connections made. The overall behavior of a system depends not only on the characteristics of single neurons, and may primarily by interaction between units, connected by the decision. By a large number of connections between the cells of non-simulated brain limitations. Associative memory limitations of a typical example of non-(3) characterization of artificial neural network is adaptive, self-organizing, self-learning ability. Neural networks can not only deal with the changes of information, but also process information the same time, nonlinear dynamic system itself is also changing. Iterative process is frequently used in describing the evolution of dynamical systems.(4) Non-convexity of the direction of the evolution of a system, under certain conditions, will depend on a particular state function. Such as energy function, and its extreme value corresponding to the state of the system more stable. Non-convexity of this function is more than one extremum, this system has multiple stable equilibrium, which will cause the system to the evolution of diversity.Artificial neural network, neural processing unit can be expressed in different objects, such as features, letters, concepts, or some interesting abstract patterns. The type of network processing unit is divided into three categories: input units, output units and hidden units. Input unit receiving the signal and data outside world; output unit for processing the results to achieve the output; hidden unit is in between the input and output units can not be observed from outside the system unit. Neurons and the connection weights reflect the strength of the connections between elements of information representation and processing reflected in the network processing unitconnected relationships. Artificial neural network is a non-procedural, adaptability, the brain's information processing style, its essence is transformation through the network and dynamic behavior is a parallel distributed information processing, and to varying degrees and levels mimic brain information processing system. It is involved in neural science, thinking, science and artificial intelligence, computer science and other interdisciplinary fields. Artificial neural networks are parallel distributed systems, using traditional artificial intelligence and information processing technology is completely different mechanism to overcome the traditional symbol of artificial intelligence-based logic in dealing with intuition, unstructured information deficiencies, adaptive, Self-organization and the characteristics of real-time learning.Artificial Neural Network – HistoryIn 1943, psychologist WSMcCulloch mathematical logician W. Pitts neural network and the establishment of a mathematical model, called the MP model. They put forward by MP model neurons and network structure of formal mathematical description of methods, that a single neuron can perform logic functions, thus creating the era of artificial neural network. In 1949, psychologists proposed the idea of synaptic strength variable. 60 years, artificial neural network to the further development of improved neural network models have been proposed, including the sensors and the adaptive linear element, etc.. M. Minsky and so careful analysis of the sensor represented by the neural network system capabilities and limitations, the in 1969 published a "Perceptron" book, pointed out that the sensor can not solve the issue of higher order predicate. Their argument has greatly influenced research in neural networks, combined with serial computers and artificial intelligence at the achievements made to cover up the development of new computer and artificial intelligence, new ways of necessity and urgency to the research of artificial neural networks at a low ebb . In the meantime, some artificial neural network remains committed to the study, researchers proposed to adapt resonance theory (ART Wang), Zi Zuzhiyingshe, Ren Zhi machine network, while for the neural network Shuxue research. More research and development of neural network research foundation. In 1982, California Institute of Technology physicist JJHopfield proposed Hopfield neural grid model, the concept of "computational energy" concept gives the network stability of the judge. In 1984, he made continuous time Hopfield neural network model for the neural computer research done pioneering work to create a neural network for associative memory and optimization of computing new ways to effectively promote the study of neural networks, In 1985, there are scholars of the Boltzmann model, the use of statistical thermodynamics in the study simulated annealing technology to ensure the overall stability of the whole system tends to point. 1986 to study the microstructure of cognition, proposed the theory of parallel distributed processing. Artificial neural networks in various countries of the importance attached by the U.S. Congress passed a resolution to January 5, 1990 began with a decade as the "brain decade", the international research organization called on its members to "brain of the 10 in "to the global behavior. In Japan's"real-world computing (RWC)" project, the artificial intelligence research has become an important part.The main consideration of artificial neural network model topology of the network connection, the characteristics of neurons, learning rules. Currently, nearly 40 kinds of neural network model, including back propagation network, perceptron, self organizing maps, Hopfield networks, Boltzmann machines, meet the resonance theory. According to the connection topology, neural network model can be divided into:(1) before the network to the network each neuron for the former level of input and output to the next level, network, no feedback, you can use a directed loop-free graph. This network signal from the input space and output space transform its information processing capabilities from a simple nonlinear function of several complex. Network structure is simple, easy to implement. Back-propagation network is a typical feedforward network.(2) feedback network network neurons have feedback, you can use an undirected complete graph. This neural network information processing is the state of transformation, you can deal with dynamic systems theory. Stability of the system is closely related with the associative memory function. Hopfield networks, Boltzmann machines belong to this category.Neural network learning is an important part of its adaptability is achieved through learning. According to the environmental changes in the value of the right to make adjustments to improve the system's behavior. Hebb proposed by the Hebb learning rule neural network learning algorithm for the foundation. Hebb learning rule that eventually occurred in the synapses between neurons, synaptic contacts with the synaptic strength of neuronal activity before and after the change. On this basis, it proposed a variety of learning rules and algorithms to meet the needs of different network models. Effective learning algorithm, making neural network connection weights through the adjustment, construction of the objective world of the intrinsic representation, forming a unique information processing method, information Cunchu and processing reflected in the network connection.According to different learning environment, neural network learning methods can be divided into supervised learning and unsupervised learning. In monitoring the study, the training sample data added to the network input side, while the corresponding desired output and network output compared Dedao error signal, thereby Kongzhiquanzhi connection strength adjustments, Jing Hou several training convergence to a determine the weights. When the sample situation changes, the weights can be modified by learning to adapt to the new environment. Use supervised learning back propagation neural network model has the network, and HC. Non-supervised learning, the prior is not a given standard sample placed directly into the network environment, learning stage and become one session. At this point, learnto obey the law of change in the evolution equation of connection weights. Non-supervised learning the most simple example is the Hebb learning rule. Competitive learning rule is a more complex example of non-supervised learning, which is based on the established clustering weight adjustment. Self-organizing map, resonance theory network to adapt to competition learn about all the typical model.Study of nonlinear dynamics of neural networks, the main use of dynamic systems theory, nonlinear programming theory and statistical theory to analyze the evolutionary neural network attractor nature of the process and to explore the neural network of collaborative behavior and collective computing to understand neural information processing mechanism. To investigate the neural networks and fuzzy in the integrity of information in terms of dealing with the possibility of chaos theory concepts and methods could be useful. Chaos is a very difficult to precisely defined mathematical concept. Generally speaking, the "chaos" refers to the dynamic system described by deterministic equations demonstrated the non-deterministic behavior, or call to determine the randomness. "Certainty" because it causes from within and not outside noise or interference generated, and "random" refers to the irregular, unpredictable behavior, can only be described using statistical methods. The main features of chaotic dynamic system is its state of sensitive dependence on initial conditions, reflect the inherent randomness of chaos. Chaos theory is the description of chaotic behavior with the basic theory of nonlinear dynamical systems, concepts, methods, and it is to understand the dynamic complexity of the system for their own actions with the outside world and its material, energy and information exchange process in the inner structured, and not external and accidental behavior, chaotic state is a steady state. Steady state chaotic dynamical systems, including: static, steady volume, periodicity, quasi-period and chaotic solution. Lorenz is the overall stability and local instability in the results of the combination, called the strange attractor. A strange attractor has the following characteristics: (1) strange attractor is an attractor, but it is neither fixed points nor periodic solutions; (2) strange attractor is indivisible, that can not be divided into two and two or more attractors; (3) is very sensitive to its initial value, different initial values will lead to very different behavior.Artificial neural network - advantagesArtificial neural network features and advantages mainly in three aspects:First, self-learning function. For example, in pattern recognition, only the first of many different images in the model and the corresponding results should be identified input of artificial neural network, the network will be through self-learning function, and slowly learn to identify similar images. Self-learning function for the forecasts of particular importance. Expected future human artificial neural network computer will provide economic forecast, market forecasting, prediction efficiency, the application of the future is very bright.Second, with the association storage. Artificial neural network feedback network can achieve this association.Third, find the optimal solution with high capacity. Find the optimal solution of a complex problem, often requires a great amount of computation, the use of a problem for the design of a feedback type artificial neural network, play a computer's high-speed computing power, may soon find the optimal solution.Artificial neural network - researchArtificial Neural NetworksNeural network research can be divided into theoretical and applied research on two areas.Theory can be divided into the following categories:1, using neuropsychological and cognitive science of human thinking and intelligence mechanism.2, the neural basis of theoretical research, using mathematical methods to explore a more complete functional performance, superior of the neural network model, in-depth study and performance of network algorithms, such as: stability, convergence, fault tolerance, robustness, etc.; Development mathematical theory of the new network, such as: neural network dynamics, nonlinear neural farms.Applied research can be divided into the following categories:1, neural network software simulation and hardware implementation of the study.2, neural network applications in all areas of research. These areas include:Pattern recognition, signal processing, knowledge engineering, expert system, optimize, robot control. With the neural network theory and the theory itself, the continuous development of relevant technology, application of neural networks will be more in-depth.Artificial Neural Networks - ApplicationNeural networks more and more attention recently because it was solved by the complexity of the problem provides a relatively simple and effective way. Neural networks can easily solve problems with hundreds of parameters (of course the actual existence of the neural network organisms than the procedures described here simulated neural networks are much more complex). Neural network used in two problems: classification and regression. In the structure, a neural network can be divided into input layer, output layer and hidden layer (see Figure 1). Each input layernode corresponds to a forecast of a variable. Output layer of nodes corresponding to the target variable can have more than one. In the input layer and output layer is hidden layer (the neural network is not visible for the user), number of layers and each layer of hidden layer nodes determines the number of neural network complexity.Figure 1 A neural networkIn addition to the input layer nodes, neural network, each node in front of it with a lot of nodes (called the node of input nodes) connected together, each connection corresponds to a weight Wxy, the value of this node is the node through which all input The value of the weights with the corresponding product and as a function of the input and get our activities in this function is called function or squeezing function. Figure 2, the output node 4 to node 6, the value can be calculated as follows:W14 * Node 1, the value of the + W24 * value of node 2Neural network each node can be expressed as predictor variables (nodes 1,2) value or the value of the portfolio (nodes 3-6). Attention to the value of the node 6 is no longer a linear combination of node 1,2, because the data passed in the hidden layer using the activity function. In fact, if there are no active function, then neural network is equivalent to a linear regression function, if the activity of a particular function is a nonlinear function, neural network that it is equivalent to logistic regression.Adjust the weights of connections between nodes is established (also called training) neural network to do work. The first and most basic method of weight adjustment feedback law is wrong, and now there are more changes in the new gradient method, Newton method, Levenberg-Marquardt method, and genetic algorithms. Regardless of the kind of training methods, we need to have some parameters to control the training process, such as training to prevent overtraining and control the pace.Figure 2 weighted Wxy the neural networkDecided to neural network topology (or architecture) is contained in the hidden layer and the number of nodes and connections between nodes. To design a neural network from scratch, have to decide the number of hidden layers and nodes, activities in the form of the function, as well as the right to redo that limit, of course, if using sophisticated software tools, he will help you decide these things. In many types of neural networks, the most commonly used to spread the former type neural network, which is ahead of us the kind depicted in the icon. People talk in detail below, in order to facilitate the discussion assumes that only contain a layer of hidden nodes. That the error feedback type training method is the change in slope of the simplified method, the process is as follows: prior to the dissemination of: data from input to output of the process is a front to back of the transmission process, after the value of a node connected to the node through which pass in front of come, then the values of theweights in accordance with the size of all the weighted input event function and then get the new value, and further spread to the next node.Feedback: When the output value of the node we expect the value of the difference, that is, an error occurs, the neural network would "learn" (learning from mistakes). We can weight between nodes connected as a node after the previous node on the "trust" level (of his own down the output of a node in front of him which is more vulnerable to the impact of input nodes). Learning are punitive approach, the process is as follows: If a node output error, then he see his error by which (some) input node of the impact caused, is not his most trusted node (the highest weight of node) in framing him (make him wrong), if the trust will have to lower his value (reducing weight), punish them, while those who make the right recommendations increase the trust value of nodes. Nodes who received punishment, he also needs the same way to further punish it in front of the node. Move forward one step on the way to spread until the punishment until the input nodes.Focus on the training must be repeated for each record this step, using the former to the spread of the output value, if an error occurs, then use the feedback method to learn. When the training set a record for each run over again, people said the completion of a training cycle. To complete the training of neural networks may take many months training period, often several hundred. After training by the neural network training set is found by the model, describes the training focused on response variables affected by the changes of predictor variables.As the hidden layer neural networks are too many variable parameters, if the training time long enough, then neural network training set is possible to all the details of information "in mind" down, Er not overdo the detail to establish a regular model only has We call this situation as overtraining. Obviously this "model" for training has a high accuracy of assembly, and once left the training set applied to other data, it is possible accuracy declined sharply. To prevent this training over the situation, one must know when to stop training. In some software implementations of the same training courses will use a test set to computational neuroscience network in this test set the correct rate, once the correct rate Buzaishenggao even started to decline, it feels that the neural network has achieved good The state has to stop training.The curve in Figure 3 can be used to help people understand why the test set to prevent the emergence of excessive training. As can be seen in Figure training set and test set error rate increased with training in the beginning, continue to lower the increase of cycle, the test set error rate reached a trough Instead, it began Shang Sheng, people think it started to rise the moment Jiu Shi should stop training the moment.Neural network training cycle increases the accuracy of the changesNeural networks and statistical methods, in essence, there are many differences. Neural Network for a lot more than the statistical methods. Figure 4, there are 13 parameters (9 and 4 weight restrictions). Because so many parameters, through various combinations of parameters to affect the output, so difficult that a neural network model to make visual interpretation. Neural networks is in fact as a "black box" to use, not to manage "box" inside what is simply used on the line. In most cases, this restriction is acceptable. Such as banks may need a handwriting recognition software, but he did not need to know why these lines together is a person's signature, while a similar not. In many complex problems such as high degree of chemical testing, robotics, simulation of financial markets, and language image recognition, neural networks and other fields have achieved very good results.Another advantage of neural network is easily implemented on parallel computers, can he be assigned to different CPU nodes in parallel computing.When using the neural network points to note: first, the neural network is difficult to explain, has yet to make a clear interpretation of the neural network methodology.Second, the neural network will learn too, to train the neural network must be appropriate to use a neural network can be critically evaluated the methods, such as the previously mentioned methods and cross-validation test set method. This is mainly due to neural network is too flexible, too many variable parameters, if given enough time, he almost can "remember" anything.Third, unless the problem is very simple to train a neural network may need considerable time to complete. Of course, once the neural network was good, and the forecasts do with it, or will soon be running.Fourth, the establishment of neural networks need a lot of work to do data preparation.A very misleading myth is that no matter what data neural networks can work well and make accurate predictions. This is inaccurate, in order to obtain accurate models of the data must be careful cleaning, sorting, transformation, selection, etc., of any data mining is that the neural network with particular emphasis on this point. Such as neural networks require that all input variables must be 0-1 (or -1 - +1) between the real number, so as the "area" like the text data need to be done only after the necessary processing for neural networks input.译文人工神经网络人工神经网络-基本特征由大量处理单元互联组成的非线性、自适应信息处理系统。