NDLI: Algorithm of fertilization knowledge model based on fuzzy-neural network

Content Provider	IEEE Xplore Digital Library
Author	Yang Yushu Cao Ran Liu Wenyang
Copyright Year	2010
Description	Author affiliation: Engineering college, Northeast Agricultural University, Haerbin, China (Yang Yushu; Cao Ran; Liu Wenyang)
Abstract	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.
Starting Page	40
Ending Page	43
File Size	472598
Page Count	4
File Format	PDF
ISBN	9781424465828
e-ISBN	9781424465859
DOI	10.1109/ICICISYS.2010.5658551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Network structure Computational modeling Fertilizer knowledge model Artificial neural networks IP(iterative pruning) algorithm Cognition FMLP(Fuzzy Neural Network)
Content Type	Text
Resource Type	Article

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