人工神经网络外文翻译

附录二英文参考文献原文

Artificial Neural Networks

Artificial Neural Networks - Basic Features

Composed 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 unit