Research on Optimization System of Machine Learning Algorithm to Improve Wireless Power Transmission

As wireless power transmission systems are widely used in modern communications, improving their performance and reliability has become an urgent task. However, frequent failures in the system pose challenges to communication quality and reliability. Traditional fault detection methods often rely on rules or manually defined features, which are difficult to cope with complex and changeable actual environments. To address this challenge, this study proposes a novel machine learning model named C-BiLSTM to improve the optimization of wireless power transmission systems. The C-BiLSTM model synergizes features from a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM). This sophisticated neural network effectively leverages the CNN's adept feature extraction and the BiLSTM's temporal modeling prowess for enhancing wireless power transmission. The transmission signals of the system are used for fault analysis and prediction. Specifically, the model accepts a one-dimensional transmission signal sequence as input, extracts local features through CNN, captures long-term dependencies through BiLSTM, and finally outputs fault classification or prediction results through a fully connected layer to provide a basis for subsequent fault processing. The C-BiLSTM model combines CNN and BiLSTM to accurately predict and analyze faults in wireless power transmission systems. The method of this study not only improves the accuracy of fault prediction, but also reduces the dependence on experts in related fields, which has higher practical significance.