Hybrid Satin Bowerbird–Egret Swarm optimization driven iterative temporal CNN for high-accuracy analog circuit fault diagnosis

Diagnosing analog circuit faults is essential to maintaining the stability and dependability of contemporary electronic systems. This paper proposes a novel hybrid deep learning framework, termed SbEO-ITCNN, which integrates Satin Bowerbird Optimization (SBO) and Egret Swarm Optimization (ESO) for efficient hyperparameter tuning of an Iterative Temporal Convolutional Neural Network (ITCNN). Initially, Wrapped Discrete Wavelet Transform (W-DWT) is employed to extract multi-resolution time–frequency features, effectively reducing noise and feature redundancy. The extracted features are then classified using ITCNN, which enhances temporal feature learning and improves classification performance. To further optimize the model, the proposed SbEO algorithm balances exploration and exploitation for robust parameter tuning. The proposed method is validated on multiple analog circuits, including Sallen–Key band-pass, Leapfrog, Butterworth low-pass, and Biquad high-pass filters. Experimental results demonstrate superior performance, achieving classification accuracies of 99.62 %, 99.21 %, 99.41 %, and 99.36 %, respectively, along with reduced computational time compared to existing techniques.