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By M. Han, X. S. Tai

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1 establishment of the artificial neural network model Network layer includes input, output and hidden layer. 2 artificial neural network evaluation algorithm of building energy saving As figure 1 shows artificial neural network, the nonlinear relationship between output and input of each node can be calculated with Sigmoid function, namely: f(x) = [1+e-x]-1 (15) 32 Advanced Research in Civil Engineering, Materials, Machinery and Applied Technologies The sample p of hidden layer is: n ypk = f( ∑ w jk u pj ), k∈(1,12) (16) j =1 The sample of output layer is: m b′P = f( ∑ wk y pk ) ypk (17) j =1 In formula(17), j∈(1, n); k∈(1, 12), wjk expresses the connection weights from the jth node of input layer to the kth node of hidden layer, wk expresses the connection weights from the jth node of hidden layer to the kth node of onput layer.

The deployable emergency bridge is deploying smoothly under the driving cable force and torsion spring of the walking-slab. The completely deployed time of the model is 30 seconds in Adams, and the states of the deploying process in different moment are shown in Fig. (a-d). 7. Deploying direction Fixed bar (a) stowed; (b) Partly deployed; (c) Partly deployed; 36 Advanced Research in Civil Engineering, Materials, Machinery and Applied Technologies (d) deployed completely Fig. 4. At each stage of the deploying process (a-d) The ability of pick the ball robot has reached theoretical calculation expected.

2009), p. K. B. Liu and Y. Sun: J. Haihe Water Resources. Vol. 5(2009), p. M. Wei: J. Journal of Hebei Engineering and Technical College. (2003), p. 1 Applied Mechanics and Materials Vol. com Keywords: limiting factors; multi-attribute utility function;shortest path algorithm Abstract. This paper discussed the main limiting factors that affects the transportion route selection of heavy and oversize cargo and established the cost and time dual objective linear model on the basis of these limiting factors.

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