An artificial neural network with partitionable outputs

Computers and Electronics in Agriculture

Volumn 16, Issue 1, 1996, Pages 39-46

  Guan, Biing T ^a   Gertner, George Z ^b  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Ecological model; Error budget; Mechanistic forest growth model; Neural network; Partitionable output; Uncertainty assessment

Indexed keywords

BUDGET CONTROL; ECOLOGY; ERRORS; ESTIMATION; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY;

ECOLOGICAL MODEL; ERROR BUDGET; MECHANISTIC FOREST GROWTH MODEL; PARTITIONABLE OUTPUT; UNCERTAINTY ASSESSMENT;

FEEDFORWARD NEURAL NETWORKS;

MONTE CARLO METHOD; NEURAL NETWORK; PARTITIONABLE OUTPUT; UNCERTAINTY ASSESSMENT;

EID: 0030417945     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0168-1699(96)00025-7     Document Type: Article

References (13)

1. Baba, N. (1989) A new approach for finding the global minimum of error function of neural networks. Neural Network, 2: 367-373.
2. Baffes, P.T. (1989) Nets User's Guide. Version 2.0. Software Technology Branch, Lyndon B. Johnson Space Center, Houston, Texas.
3. Cybenko, G. (1989) Approximation by superposition of a sigmoidal function. Math. Controls Signals Syst., 2: 303-314.
4. Gertner, G. (1987) Approximating precision in simulation projections: an efficient alternative to Monte Carlo methods. Forest Sci., 33: 230-239.
5. Gertner, G., Parysow, P. and Guan, B.T. (1996) Projection variances partitioning of a conceptual forest growth model with orthogonal polynomials. Forest Sci., 42(4).
6. Guan, B.T., Gertner, G. and Kowalski, D. (1993) Modeling training site vegetation coverage probability with a random optimization procedure: an artificial neural network approach. In: Proceedings of the Conference of Applications of Artificial Neural Networks IV, Orlando, Florida, pp. 682-688.
7. Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedforward networks are universal approximators. Neural Network, 2: 359-366.
8. Shinozaki, K., Yoda, K., Hozumi, K. and Kira, T. (1964a) A quantitative analysis of plant form - the pipe model theory. I. Basic analysis. Jap. J. Forest Ecol., 14: 97-105.
9. Shinozaki, K., Yoda, K., Hozumi, K. and Kira, T. (1964b) A quantitative analysis of plant form - the pipe model theory. II. Further evidence of the theory and its application in forest ecology. Jap. J. Forest Ecol., 14: 133-139.
10. Stinchcombe, M. and White, H. (1989) Universal approximation using feedforward networks with nonsigmoid hidden layer activation functions. In: Proceedings of the 1989 International Joint Conference on Neural Networks. IEEE Press, New York, N.Y., pp. 613-617.
11. Szu, H. and Hartley, R. (1987) Fast simulated annealing. Phys. Lett. A, 122: 157-162.
12. Valentine, H. (1988) A carbon balance model of stand growth: a derivation employing pipe-model theory and the self-thinning rule. Ann. Bot., 62: 389-396.
13. White, H. (1989) Some asymptotic results of learning in single hidden-layer feedforward network models. J. Am. Stat. Assoc., 84: 1003-1013.