BP-MSM混合算法及其在森林自疏规律研究中的应用

应用生态学报 ›› 2000, Vol. ›› Issue (5): 655-659.

BP-MSM混合算法及其在森林自疏规律研究中的应用

吴承祯, 洪伟

福建林学院资源与环境学系, 南平353001

收稿日期:1999-03-19 修回日期:1999-07-19 出版日期:2000-09-25
通讯作者: 吴承祯,1970年生,博士,副教授.主要从事数量生态学与森林生态学等领域教学与科研.发表学术论文80余篇.E-mail:zjwucz@public.npptt.fj.cn
基金资助:
福建省自然科学基金资助项目(F991)

Neural network based on modified simplex method and its application in studying forest self-thinning

WU Chengzhen, HONG Wei

Department of Resources and Environment, Fujian Forestry College, Nanping 353001

Received:1999-03-19 Revised:1999-07-19 Online:2000-09-25

摘要/Abstract

摘要： 森林自然稀疏机制一般是非线性的、动态的.人工神经网络具有逼近任意非线性映射的特性.本文阐述了人工神经网络模拟森林自疏机制的可行性和不足之处,并提出了基于改进单纯形法的神经网络模型(BP-MSM混合算法)的基本原理和算法,结合山杨天然林和杉木人工林自疏实例说明了其应用.森林自疏实例应用结果表明,BP-MSM混合算法模拟森林自然稀疏机制是理想的,模拟精度较高,从而继承和发展了人工神经网络方法与理论,丰富了森林自然稀疏规律研究方法.

Abstract: The mechanism of forest self-thinning is generally nonlinear and dynamic,and the artificial neural network has the characteristic of expressing arbitrary nonlinear mapping.In this paper, the feasibility and limitation of artificial neural network used to simulating forest self-thinning was expounded,and the principle and algorithms of the neural network model based on modified simplex method (BP-MSM mixed algorithms) for modeling forest self-thinning were described.Its applications in self-thinning of Populus tremula natural forest Cunninghamia lanceolata plantation were illustrated. The results of forest self-thinning examples show the BP MSM mixed algorithms were satisfactory in simulating forest self-thinning,and its precision was higher, which develops the method and theory of artificial neural network,and enriches the simulating method of forest self-thinning.

吴承祯, 洪伟. BP-MSM混合算法及其在森林自疏规律研究中的应用[J]. 应用生态学报, 2000, (5): 655-659.

WU Chengzhen, HONG Wei. Neural network based on modified simplex method and its application in studying forest self-thinning[J]. Chinese Journal of Applied Ecology, 2000, (5): 655-659.

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