1 M-P神经元模型的工作原理和几何意义
1943年,MoCulloch和Pitts[1]根据神经元传递规律,第一次提出了神经元的数学模型.M-P神经元模型一直沿用至今,它对神经网络的发展起到了奠基性的作用.每个神经元的状态由M-P方程决定:S=f(∑W X -θ)...1 M-P神经元模型的工作原理和几何意义
1943年,MoCulloch和Pitts[1]根据神经元传递规律,第一次提出了神经元的数学模型.M-P神经元模型一直沿用至今,它对神经网络的发展起到了奠基性的作用.每个神经元的状态由M-P方程决定:S=f(∑W X -θ),θ为阈值,f为激励函数,一般取符号函数.令:它代表了n维空间中,以X为坐标变量,以W为坐标系数,θ为常数项的一个超平面.当样本点X落入超平面的正半区,即I(X)>0时,有f(I)＝1;当样本点X落入超平面的负半区,即I(X)<0时,有f(I)＝0.从分类的角度看,一个神经元按输入将样本划分成为两类(0和1).现在广泛使用的BP模型采用Sigmoid函数作为激励函数,但是它没有改变神经元分类的本质.神经网络实际上就是多个神经元组织起来的一种网状结构.展开更多
European mountains are particularly sensitive to climatic disruptions and land use changes.The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possibl...European mountains are particularly sensitive to climatic disruptions and land use changes.The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possible impacts on ecosystem services,LUCC models offer new opportunities for land managers to adapt or mitigate their strategies.Assessing the spatial uncertainty of future LUCC is crucial for the definition of sustainable land use strategies. However, the sources of uncertainty may differ, including the input parameters, the model itself, and the wide range of possible futures. The aim of this paper is to propose a method to assess the probability of occurrence of future LUCC that combines the inherent uncertainty of model parameterization and the ensemble uncertainty of the future based scenarios. For this purpose, we used the Land Change Modeler tool to simulate future LUCC on a study site located in the Pyrenees Mountains(France) and two scenarios illustrating two land use strategies. The model was parameterized with the same driving factors used for its calibration. The definition of ‘static vs. dynamic' and ‘quantitative vs.qualitative(discretized)' driving factors, and their combination resulted in four parameterizations. The combination of model outcomes produced maps of the spatial uncertainty of future LUCC. This work involves adapting the definition of spatial uncertainty in the literature to future-based LUCC studies. It goes beyond the uncertainty of simulation models by integrating the uncertainty of the future to provide maps to help decision makers and land managers.展开更多
文摘1 M-P神经元模型的工作原理和几何意义
1943年,MoCulloch和Pitts[1]根据神经元传递规律,第一次提出了神经元的数学模型.M-P神经元模型一直沿用至今,它对神经网络的发展起到了奠基性的作用.每个神经元的状态由M-P方程决定:S=f(∑W X -θ),θ为阈值,f为激励函数,一般取符号函数.令:它代表了n维空间中,以X为坐标变量,以W为坐标系数,θ为常数项的一个超平面.当样本点X落入超平面的正半区,即I(X)>0时,有f(I)＝1;当样本点X落入超平面的负半区,即I(X)<0时,有f(I)＝0.从分类的角度看,一个神经元按输入将样本划分成为两类(0和1).现在广泛使用的BP模型采用Sigmoid函数作为激励函数,但是它没有改变神经元分类的本质.神经网络实际上就是多个神经元组织起来的一种网状结构.
基金supported the HumanEnvironment Observatory of the Haut-Vicdessos (Labex DRIIHM - OHM Haut-Vicdessos)the MODE RESPYR project (ANR 2010 JCJC 1804-01)the SAMCO Project (ANR-12-SENV-0004) founded by the French National Science Agency (ANR)
文摘European mountains are particularly sensitive to climatic disruptions and land use changes.The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possible impacts on ecosystem services,LUCC models offer new opportunities for land managers to adapt or mitigate their strategies.Assessing the spatial uncertainty of future LUCC is crucial for the definition of sustainable land use strategies. However, the sources of uncertainty may differ, including the input parameters, the model itself, and the wide range of possible futures. The aim of this paper is to propose a method to assess the probability of occurrence of future LUCC that combines the inherent uncertainty of model parameterization and the ensemble uncertainty of the future based scenarios. For this purpose, we used the Land Change Modeler tool to simulate future LUCC on a study site located in the Pyrenees Mountains(France) and two scenarios illustrating two land use strategies. The model was parameterized with the same driving factors used for its calibration. The definition of ‘static vs. dynamic' and ‘quantitative vs.qualitative(discretized)' driving factors, and their combination resulted in four parameterizations. The combination of model outcomes produced maps of the spatial uncertainty of future LUCC. This work involves adapting the definition of spatial uncertainty in the literature to future-based LUCC studies. It goes beyond the uncertainty of simulation models by integrating the uncertainty of the future to provide maps to help decision makers and land managers.
