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Abstract As matter of fact，the evolution system of coal and gas outbursts is a typical nonlinear dynamical one；there are many factors such as stress，thickness variation of seam，geological fault，etc. influencing it，and these factors are correlative. It is necessary to build a nonlinear artificial neural network(ANN) to recognize the pattern of coal and gas outbursts and to predicate coal and gas outbursts intensity. A self-adaptive wavelet neural network for recognizing pattern of coal and gas outbursts and for predicating coal and gas outbursts intensity has been built by considering different coal seam and gas conditions，which can generate the neural element numbers automatically and can avoid the jamming for determining the element number in BP network artificially. This ensures the reliability and intelligence of recognition and predication. It is verified by some examples that the model has a high accuracy for recognition and predication；and it is valuable for generalizations and applications.
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Received: 17 April 2007
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