外文资料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 brains 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 Ins