| 摘要 |
A plurality of neural networks are coupled to an output neural network, or judge network, to form a clustered neural network. Each of the plurality of clustered networks comprises a supervised learning rule back-propagated neural network. Each of the clustered neural networks are trained to perform substantially the same mapping function before they are clustered. Following training, the clustered neural network computes its output by taking an "average" of the outputs of the individual neural networks that make up the cluster. The judge network combines the outputs of the plurality of individual neural networks to provide the output from the entire clustered network. In addition, the output of the judge network may be fed back to each of the individual neural networks and used as a training input thereto, in order to provide for continuous training. The use of the clustered network increases the speed of learning and results in better generalization. In addition, clustering multiple back-propagation networks provides for increased performance and fault tolerance when compared to a single unclustered network having substantially the same computational complexity. The present invention may be used in applications that are amenable to neural network solutions, including control and image processing applications. Clustering of the networks also permits the use of smaller networks and provides for improved performance. The clustering of multiple back-propagation networks provides for synergy that improves the properties of the clustered network over a comparably complex non-clustered network.
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