Titlebook: Artificial Neuronal Networks; Application to Ecolo Sovan Lek,Jean-Fran?ois Guégan Book 2000 Springer-Verlag Berlin Heidelberg 2000 Tempo.al

組成

Evolutionarily Optimal Networks for Controlling Energy Allocation to Growth, Reproduction and Repairquantitatively, using an evolutionary optimization approach, the so-called disposable soma theory of ageing (Kirkwood 1981). This theory affirms that the senescence of an organism with age is due to insufficient repair caused by evolutionarily profitable diversion of energy to the organism’s other n

模范

https://doi.org/10.1007/978-3-642-57030-8Tempo; algorithms; artificial neural network; ecology; ecosystem; ecosystem ecology; evolution; fuzzy; genet

BOOM

978-3-642-63116-0Springer-Verlag Berlin Heidelberg 2000

業(yè)余愛好者

MULTI

Predicting Presence of Fish Species in the Seine River Basin Using Artificial Neuronal Networksey can be considered to be good indicators of the health of aquatic ecosystems (Fausch et al. 1990). This paradigm is the basis for using biological monitoring of fish to assess environmental degradation (Karr 1987).

Connotation

Performance Comparison between Regression and Neuronal Network Models for Forecasting Pacific Sardinvasive characteristics which can undermine one’s ability to conduct accurate forecasts. In some cases the span or resolution of available data can limit development or use of a particular kind of model.

有害

兒童

美麗的寫

HARD

https://doi.org/10.1007/978-3-642-82880-5al, mathematical, and statistical methods to techniques originating from artificial intelligence (Ackley et al. 1985) like expert systems (Bradshaw et al. 1991; Recknagel et al. 1994), genetic algorithms (d’Angelo et al. 1995; Golikov et al. 1995) and artificial neuronal networks, i.e. ANN (Colasant