外文翻译---人工神经网络(编辑修改稿)

外文翻译---人工神经网络(编辑修改稿)内容摘要：

s with before and after the activity and synaptic neuron changes. Based on this, people put forward various learning rules and algorithm, in order to adapt to the needs of different work model. Effective learning algorithm, and makes the god The work can through the weights between adjustment, the structure of the objective world, said the formation of inner characteristics of information processing method, information storage and processing reflected in the work connection. According to the learning environment is different, the study method of the neural work can be divided into learning supervision and unsupervised learning. In the supervision and study, will the training sample data added to the work input, and the corresponding expected output and work output, in parison to get error signal control value connection strength adjustment, the DuoCi after training to a certain convergence weights. While the sample conditions change, the study can modify weights to adapt to the new environment. Use of neural work learning supervision model is the work, the sensor etc. The learning supervision, in a given sample, in the environment of the work directly, learning and working stages bee one. At this time, the change of the rules of learning to obey the weights between evolution equation of. Unsupervised learning the most simple example is Hebb learning rules. Competition rules is a learning more plex than learning supervision example, it is according to established clustering on weights adjustment. Selforganizing mapping, adapt to the resonance theory is the work and petitive learning about the typical model. Analysis method Study of the neural work nonlinear dynamic properties, mainly USES the dynamics system theory and nonlinear programming theory and statistical theory to analysis of the evolution process of the neural work and the nature of the attractor, explore the synergy of neural work behavior and collective puting functions, understand neural information processing mechanism. In order to discuss the neural work and fuzzy prehensive deal of information may, the concept of chaos theory and method will play a role. The chaos is a rather difficult to precise definition of the math concepts. In general, chaos it is to point to by the dynamic system of equations describe deterministic performance of the uncertain behavior, or call it sure the randomness. Authenticity because it by the intrinsic reason and not outside noise or interference produced, and random refers to the irregular, unpredictable behavior, can only use statistics method description. Chaotic dynamics of the main features of the system is the state of the sensitive dependence on the initial conditions, the chaos reflected its inherent randomness. Chaos theory is to point to describe the nonlinear dynamic behavior with chaos theory, the system of basic concept, methods, it dynamics system plex behavior understanding for his own with the outside world and for material, energy and information exchange process of the internal structure of behavior, not foreign and accidental behavior, chaos is a stationary. Chaotic dynamics system of stationary including: still, stable quantity, the periodicity, with sex and chaos of accurate solution... Chaos rail line is overall stability and local unstable bination of results, call it strange attractor. A strange attractor has the following features: (1) some strange attractor is a attractor, but it is not a fixed point, also not periodic solution。 (2) strange attractor is indivisible, and that is not divided into two and two or more to attract children. (3) it to the initial value is very sensitive, different initial value can lead to very different behavior. superiority The artificial neural work of characteristics and advantages, mainly in three aspects: first, selflearning. For example, only to realize image recognition that the many different image model and the corresponding should be the result of identification input artificial neural work, the work will through the selflearning function, slowly to learn to distinguish similar images. The selflearning function for the forecast has special meaning. The prospect of artificial neural work puter will provide mankind economic forecasts, market forecast, benefit forecast, the application ou。