Algorithm of Fertilization Knowledge Model Based on Fuzzy-Neural Network Fertilization Knowledge Model
When traditional mathematical statistic method is used to build fertilization model, the structure design and factor choosing largely rely on prior knowledge of experts in the field. Consequently, the result is somewhat casual and subjective. It is very difficult to settle this universal nonlinear and uncertain problems in fertilization. A new network for fuzzy-neural system which is easy to distill the fuzzy rules is proposed. The network structure is adjusted by FBP(Fuzzy Back Propagation) learning algorithm to acquire network parameters and variable weights of the initial fertilization model. For an optimal fertilization model comes into being, IIP(Improved iterative pruning) algorithm which is applied can lessen the network structure and reduce the complexity of compute to speed up the respond rate of output. Soybean experiments in different plants and years show that this approach can build very accurate model without any prior knowledge, which contributes as theoretical in countryside.
Network structure FMLP(Fuzzy Neural Network) IP(iterative pruning) algorithm fertilizer knowledge model
YANG Yushu Cao ran Liu Wenyang
Engineering college Northeast Agricultural University Haerbin, China
国际会议
厦门
英文
40-43
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）