Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield

A particular type of “Artificial neural network (ANN)â€, viz. Multilayered feedforward artificial neural network (MLFANN) has been described. To train such a network, two types of learning algorithms, namely Gradient descent algorithm (GDA) and Conjugate gradient descent algorithm (CGDA), have been discussed. The methodology has been illustrated by considering maize crop yield data as response variable and total human labour, farm power, fertilizer consumption, and pesticide consumption as predictors. The data have been taken from a recently concluded National Agricultural Technology Project of Division of Agricultural Economics, I.A.R.I., New Delhi. To train the neural network, relevant computer programs have been written in MATLAB software package using Neural network toolbox. It has been found that a three-layered MLFANN with (11,16) units in the two hidden layers performs best in terms of having minimum mean square errors (MSE) for training, validation, and test sets. Superiority of this MLFANN over multiple linear regression (MLR) analysis has also been demonstrated for the maize data considered in the study. It is hoped that, in future, research workers would start applying not only MLFANN but also some of the other more advanced ANN models, like ‘Radial basis function neural network’, and ‘Generalized regression neural network’ in their studies.Crop Production/Industries,

Syntactic Complexity of Circular Semi-Flower Automata

We investigate the syntactic complexity of certain types of finitely generated submonoids of a free monoid. In fact, we consider those submonoids which are accepted by circular semi-flower automata (CSFA). Here, we show that the syntactic complexity of CSFA with at most one `branch point going in' (bpi) is linear. Further, we prove that the syntactic complexity of $n$-state CSFA with two bpis over a binary alphabet is $2n(n+1)$

Oscillations in active region fan loops: Observations from EIS/{\it Hinode} and AIA/SDO

Active region fan loops in AR 11076 were studied, in search of oscillations, using high cadence spectroscopic observations from EIS on board Hinode combined with imaging sequences from the AIA on board SDO. Spectra from EIS were analyzed in two spectral windows, \FeXII 195.12 \AA and \FeXIII 202.04 \AA along with the images from AIA in 171 \AA and 193 \AA channels. We find short ($<$3 min) and long ($\approx$9 min) periods at two different locations. Shorter periods show oscillations in all the three line parameters and the longer ones only in intensity and Doppler shift but not in line width. Line profiles at both these locations do not show any visible blue-shifted component and can be fitted well with a single Gaussian function along with a polynomial background. Results using co-spatial and co-temporal data from AIA/SDO do not show any significant peak corresponding to shorter periods, but longer periods are clearly observed in both 171 \AA and 193 \AA channels. Space-time analysis in these fan loops using images from AIA/SDO show alternate slanted ridges of positive slope, indicative of outward propagating disturbances. The apparent propagation speeds were estimated to be 83.5 $\pm$ 1.8 \kms and 100.5 $\pm$ 4.2 \kms, respectively, in the 171 \AA and 193 \AA channels. Observed short period oscillations are suggested to be caused by the simultaneous presence of more than one MHD mode whereas the long periods are suggested as signatures of slow magneto-acoustic waves. In case of shorter periods, the amplitude of oscillation is found to be higher in EIS lines with relatively higher temperature of formation. Longer periods, when observed from AIA, show a decrease of amplitude in hotter AIA channels which might indicate damping due to thermal conduction owing to their acoustic nature.Comment: Accepted for publication in Solar Physic