Self Organizing Map (SOM) neural network based on novel fuzzy wavelet for nonlinear function approximation

Ghadamyari, Ali; Safavi, Ali Akbar

doi:10.1109/icciautom.2011.6356744

Cited by 1 publication

(2 citation statements)

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“…In the input layer, neurons represent the inputs. In the second layer, the competitive process is done and the weight of connection is updated to choose a winner neuron (Ghadamyari and Safavi, 2011). In the input layer, the output of each neurons, x i for i=1, 2, 3, …, is connected to all neurons of competitive layer and each connection is assigned a variable weight, w ij for i=1, 2, 3, ….…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

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The self-organizing map for determination of main features related to biological yield and yield of wheat

Bijanzadeh

Mokarram²

2016

Aust J Crop Sci

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Among various methods of artificial neural networks (ANNs) and learning algorithms, self-organizing map (SOM) is one of the most popular models. The aim of this study is to classify features influencing the biological yield and yield of wheat using SOM algorithm. In SOM, according to qualitative data, the clustering tendency of yield and biological yield of wheat were investigated using 11142 data from 16 features. Data was collected from the literatures on the subject of wheat in Iran that was existed in http://sid.ir website. Results showed that when biological yield was as output, K with soil pH, irrigation regime with 1000-kernel weight and organic content (OC) with grain/spike were related to each other closely. Moreover, grain/spike and OC had closer relationship to biological yield. In contrast, negative relationship was observed between soil pH (r=-0.47) and HI (r=-0.61) with biological yield. When wheat grain yield was output of SOM model, K with soil pH, and P with OC was related to each other closely. Overall, grain/spike, P and OC were much closer related to crop yield than other parameters. Similar to biological yield, labels map showed that data classified in three classes for wheat yield and the top four rows of U-matrix were placed in class A. A clear separation was observed among class A with B and C. The characteristics of each group in the study area showed that group 2 with 0.784 (kg/m 2) had the highest yield than group 1 (0.241 kg/m 2) and group 3 (0.401 kg/m 2) so that in group 2, the amount of P (0.003 kg/m 2), OC (0.47%), pH (7.78), rainfall (492.45 mm), grain/spike (43.71) and spike/m 2 (668.21) and HI (37.53%) were higher than the other groups and related to yield directly. Our results showed that among the yield components, grain/spike was the most important features contributing to grain yield than spike/m 2 and 1000-kernel weight using SOM.

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Section: Self-organizing Map (Som)mentioning

confidence: 99%

“…5) Continuation: this process is repeated until the ultimate goal is achieved. Chosen input will be compared with all weight of connections according to the following equation (Lee et al, 2007;Ghadamyari and Safavi, 2011):…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%