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Author: search.filters.author.Supnithi, PornchaiSubject: search.filters.subject.Neural Network

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    Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model
    (2023-10-01)
    Thu, Phyo C.
    ;
    Supnithi, Pornchai
    ;
    Budtho, Jirapoom
    ;
    Saekow, Apitep
    ;
    Sopon, Thanomsak
    Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.
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    Optimal neuro-fuzzy equalizers for nonlinear channels of the perpendicular magnetic recording system
    (2021-01-01)
    Wongsathan, Rati
    ;
    Supnithi, Pornchai
    Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1-3 dB and 1.5-3.5 dB signalto-noise ratio gains at the bit-error-rate of 10−4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.
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    A Preliminary Neural Network Model for Range Spread-F Events at Chumphon Station, Thailand
    (2018-12-24)
    Thammavongsy, Phimmasone
    ;
    Phakphisut, Watid
    ;
    Supnithi, Pornchai
    In this work, we develop a preliminary neural network model for range-Type spread-F events over Chumphon station (10.7N latitude, 99.4E longitude), Thailand. The spread-F neural network model is designed with the input parameters including seasonal variations, diurnal variations, window-Averaged magnetic activity (Ap index) and window-Averaged solar activity (F10.7 index). The model is based on the ionogram data during the 24 <sup>th</sup> solar cycle from 2013 to 2016. As a result, the proposed model can provide the predicted results and the network performance of 97.8% for correct classification.
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    Prediction of foF2 using Neural Network at Thailand equatorial latitude station, Chumphon
    (2014-01-01)
    Wichaipanich, Noraset
    ;
    Supnithi, Pornchai
    This paper proposes the development of a Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at the magnetic equator over Chumphon (10.72°N, 99.37°E, dip angle 3.3°N), Thailand and then compared with the IRI model and the experimental ones. The feed forward network with backpropagation algorithm has been developed for predicting the foF2 values. The NN is trained with the daily hourly values of foF2 during the period from 2004 to 2008 and the input parameters affecting the foF2 variability including the hour number, day number, F10.7 index and sunspot number (SSN). To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI (both CCIR and URSI options) model are compared in 2009. The results show that the NN model predicts the foF2 values close to the observed data, particularly during daytime. Moreover, the NN model can predicts more accurate than the IRI model that is supported by the lower RMSE. However, the NN model provides slightly deviation of prediction during pre-sunrise hours since the observed foF2 data for NN training in this periods are fewer than those during daytime. © 2014 IEEE.