{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:40:10Z","timestamp":1765546810585},"reference-count":0,"publisher":"Engineering and Technology Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jcm"],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:p>This research work explores the Levenberg- Marquardt training algorithm used for Artificial Neural Network (ANN) optimization during training and the Bayesian Regularization algorithm for the enhanced generalized trained network in training a designed non-linear vector median filter built on Multi-Layer Perceptron (MLP) ANN called model-1 and a conventional MLP ANN called model-2. The model-1 employed in the design helps in dataset de-noising to ensure the removal of unwanted signals for the improved training dataset. An early stopping method in the ratio of 80:10:10 for training, testing, and validation to overcome the problem of over-fitting during network training was employed. First-order statistical indices, the standard deviation, root mean squared error, mean absolute error, and correlation coefficient were adopted for network training analysis and comparative analysis of the designed model-1 and model-2, respectively. Two locations, Line-of-sight (location-1) and non-Line-of-Sight (location-2), were considered where the dataset was captured. The training results from the two locations for the two models demonstrated improved prediction of signal power loss using model-1 in comparison to model-2. For instance, the correlation coefficient, which shows the strength of the predicted value to the measured values (closer to 1) establishing a strong connection, gives 0.990 and 0.995 using model-1 for location-1, training with Lavenberg-Marquardt and Bayesian Regularization algorithm, respectively and 0.965 and 0.980 for model-2 using the same algorithms. It is seen that the Bayesian regularization algorithm, which optimizes the network in accordance with the Levenberg- Marquardt algorithm, gave better prediction results. The same sequence of improved perditions using designed model-1 in comparison to model-2 were seen with training results in location-2 while also adopting other employed 1st order statistical indices.<\/jats:p>","DOI":"10.12720\/jcm.16.11.508-515","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T06:09:56Z","timestamp":1635228596000},"page":"508-515","source":"Crossref","is-referenced-by-count":1,"title":["Performance Comparison of Non-Linear Median Filter Built on MLP-ANN and Conventional MLP-ANN: Using Improved Dataset Training in Micro-Cell Environment"],"prefix":"10.12720","author":[{"name":"Department of Electronic Engineering, Howard College, University of KwaZulu-Natal, Durban-4041, South Africa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Virginia C.","family":"Ebhota","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viranjay M.","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4977","published-online":{"date-parts":[[2021]]},"container-title":["Journal of Communications"],"original-title":[],"link":[{"URL":"http:\/\/www.jocm.us\/uploadfile\/2021\/1022\/20211022023654984.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T06:10:04Z","timestamp":1635228604000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jocm.us\/show-261-1704-1.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":0,"URL":"https:\/\/doi.org\/10.12720\/jcm.16.11.508-515","relation":{},"ISSN":["2374-4367"],"issn-type":[{"type":"print","value":"2374-4367"}],"subject":[],"published":{"date-parts":[[2021]]}}}