Artificial intelligence using Nelder-Mead algorithm- based design and performance optimization of microstrip patch antenna

Artificial intelligence systems are one of the important machines in performing operations that are difficult to perform traditionally. Optimization is one of the difficult and delicate processes that AI can be used to accomplish, especially if the optimizations are too small for antennas like microstrip patch antenna. A Microstrip patch antenna is considered one of the most widely used antennas that vary from lightweight wireless devices to airplanes and airspaces applications. One of the most attractive points about those antennas is their lightweight, small size, and ease of fabrication process. Although this antenna has many advantages, it suffers from some drawbacks like low gain and limited bandwidth. In this paper, we are presenting an optimization process by using the Nelder-Mead algorithm to achieve a new design of patch antenna that offers a broader bandwidth and higher gain. This design is achieved by optimizing the dimensions of the width and the frequency of the antenna. The results show that this device is responding perfectly at 1.471GHz and the ranges of substrate dimensions and relative permittivity affect the device performance and behavior

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time