ANN- Artificial Neuron Network. I want to implement ML for faster result so seeking guidance
Hi Prasannajeet Mohanty
Implementing Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) models to locate the optimal feeding point in a patch antenna entails a systematic approach that integrates the predictive capabilities of ANN with the optimization prowess of PSO. Initially, the ANN is trained with a dataset comprising various antenna designs, feeding points, and their corresponding performance metrics (such as return loss, bandwidth, and radiation pattern). This training enables the ANN to predict the performance of new antenna designs based on their feeding point locations.
Once the ANN is adept at making accurate predictions, the PSO algorithm is employed to search for the optimal feeding point location. PSO simulates a social behavior pattern, where a population of candidate solutions (particles) explores the design space to find the optimal feeding point that maximizes the antenna's performance, guided by the predictions made by the ANN. This iterative process of evaluation (using ANN predictions) and optimization (using PSO) converges to the location of the best feeding point, ensuring enhanced antenna performance in terms of its intended operational characteristics. This integrative approach effectively combines the strengths of both ANN and PSO, leveraging ANN's predictive accuracy and PSO's optimization efficiency, to systematically identify the optimal feeding point location in patch antennas.
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