【正文】 rning, the organization and nonlinear mapping the advantages of neural work and other technology and the integration of it follows that the hybrid method and hybrid systems, has bee a hotspot. Since the other way have their respective advantages, so will the neural work with other method, and the bination of strong points, and then can get better application effect. At present this in a neural work and fuzzy logic, expert system, geic algorithm, wavelet analysis, chaos, the rough set theory, fractal theory, theory of evidence and grey system and fusion. 漢語翻譯 人工神經(jīng)網(wǎng)絡(luò) （ ArtificialNeuralNetworks，簡寫為 ANNs）也簡稱為神經(jīng)網(wǎng)絡(luò)（ NNs）或稱作連接模型（ ConnectionistModel），它是一種模范動物神經(jīng)網(wǎng)絡(luò)行為特征，進行分布式并行信息處理的算法數(shù)學(xué)模型。s real world puting (springboks claiming) project, artificial intelligence research into an important ponent. Network model Artificial neural work model of the main consideration work connection topological structure, the characteristics, the learning rule neurons. At present, nearly 40 kinds of neural work model, with back propagation work, sensor, selforganizing mapping, the Hopfield puter, wave boltzmann machine, adapt to the ear resonance theory. According to the topology of the connection, the neural work model can be divided into: (1) prior to the work before each neuron accept input and output level to the next level, the work without feedback, can use a loop to no graph. This work realization from the input space to the output signal of the space transformation, it information processing power es from simple nonlinear function of DuoCi pound. The work structure is simple, easy to realize. Against the work is a kind of typical prior to the work. (2) the feedback work between neurons in the work has feedback, can use a no to plete the graph. This neural work information processing is state of transformations, can use the dynamics system theory processing. The stability of the system with associative memory function has close relationship. The Hopfield puter, wave ear boltzmann machine all belong to this type. Learning type Neural work learning is an important content, it is through the adaptability of the realization of learning. According to the change of environment, adjust to weights, improve the behavior of the system. The proposed by the Hebb Hebb learning rules for neural work learning algorithm to lay the foundation. Hebb rules say that learning process finally happened between neurons in the synapse, the contact strength synapses parts 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