Jangfa T. zhimwang

Work place: Department of Physics, Federal University Lokoja, Lokoja, Kogi State, Nigeria

E-mail: Ituabhor.odesanya@fulokoja.edu.ng


Research Interests:


Jangfa Timothy Zhimwang is a Lecturer with Department of Physics, Federal University Lokoja, Nigeria. He holds a BSc in Physics Electronics Technology, MSc in Physics (Electronics & Communication Tech.), PGD in mathematics Education, HND. Pest Management Technology and presently a PhD candidate. He has previously worked under a research team sponsored by the Tertiary Education Trust Fund (TETFund) through the Department of Physics, University of Jos and productively written and published renowned articles in the field of Physics Electronics and Communication Technology.

Author Articles
Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

By Odesanya Ituabhor Joseph Isabona Jangfa T. zhimwang Ikechi Risi

DOI: https://doi.org/10.5815/ijcnis.2022.03.05, Pub. Date: 8 Jun. 2022

In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.

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