By Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed complaints of the eighth overseas Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers offered in all 3 volumes have been conscientiously reviewed and chosen from 651 submissions. The contributions are based in topical sections on computational neuroscience and cognitive technology; neurodynamics and complicated structures; balance and convergence research; neural community versions; supervised studying and unsupervised studying; kernel equipment and aid vector machines; combination versions and clustering; visible notion and development attractiveness; movement, monitoring and item attractiveness; traditional scene research and speech reputation; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent structures and adaptive dynamic programming; reinforcement studying and determination making; motion and motor keep an eye on; adaptive and hybrid clever structures; neuroinformatics and bioinformatics; info retrieval; info mining and data discovery; and average language processing.
Read or Download Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II PDF
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Extra resources for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II
3 Network Learning Algorithm While backpropagation with gradient descent technique is a steepest descent algorithm, the Levenberg-Marquardt algorithm is an approximation to Newton’s method . If a function V(x) is to be minimized with respect to the parameter vector x , then Newton’s method would be : Δx = −[∇ 2V ( x)]−1 ∇V ( x) where ∇ V ( x ) is the Hessian matrix and pressed as: 2 (3) ∇V (x) is the gradient. If V(x) is ex- N V ( x) = ∑ ei2 ( x) i =1 (4) Prediction of Urban Stormwater Runoff in Chesapeake Bay Using Neural Networks 31 Then it can be shown that: ∇V ( x ) = J T ( x )e ( x ) (5) ∇ 2V ( x) = J T ( x) J ( x) + S ( x) (6) where J(x) is the Jacobian matrix and N S ( x) = ∑ ei ∇ 2 ei ( x) (7) i =1 For the Gauss-Newton method it is assumed that S ( x) becomes: ≈ 0 , and the equation (3) Δx = [ J T ( x) J ( x)]−1 J T ( x)e( x) (8) The Levenberg-Marquardt modification to the Gauss-Newton method is: Δx = [ J T ( x) J ( x) + μI ]−1 J T ( x)e( x) (9) The parameter μ is multiplied by some factor (β) whenever a step would result in an increased V(x) .
Document No. EPA 841-F-03-003 (2003) 2. United States. National Research Council. Washington, DC. Urban Stormwater Management in the United States, pp. 18–20 (2008) 3. Fact Sheet 102-98 - The Chesapeake Bay: Geologic Product of Rising Sea Level. U. S. gov/fs/fs102-98/ 4. : Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998) 5. : Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed). American-Eurasian J. Agric. & Environ.
Therefore, the flood-control solutions are the major concern. Runoff prediction would provide a promising solution for flood-control. The real-time USGS data for the Four Mile Run station include both the discharge data and gage height data, which is useful for investigating their impact to the longrun discharge forecast. The runoff data was retrieved for 120 days between August 28, 2010 and December 4, 2010. The runoff discharge (cubic feet per second) data is plotted in Fig. 1, and the gage height (feet) data is illustrated in Fig.
Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II by Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)