@article{CRSIJ26000102,
                  author = {Udeagbala Remigius Ndidika, Egbonwonu Emmanuel Livinus, Okika Stephen C., Ezema D. C., Ogbodo Ikechukwu O.},
                  title = {Improving operational efficiency of power amplifier audio system using artificial neural network },
                  journal = {Cosmo Research and Science International Journal},
                  year = {2025},
                  volume = {1},
                  number = {5},
                  pages = {183-198},
                  issn = {3108-1584},
                  url = {https://cosmorsij.com/published/CRSIJ26000102.pdf},
                  abstract = {The advancement of audio systems necessitates the development of highly efficient and accurate power amplifiers capable of delivering superior sound quality with minimal distortion and power loss. This study focuses on improving the operational efficiency of Power amplifier audio system. The conventional audio system is often faced with challenges such as non-linear signal distortion, thermal instability, and inefficiency under variable load conditions. To address these limitations, an intelligent ANN-based model was developed and trained to optimize critical design parameters, including biasing conditions, gain control, feedback mechanisms, and thermal compensation. The ANN model effectively learned complex non-linear relationships between input signals and desired output characteristics, enabling real-time adjustment and adaptive performance enhancement of the amplifier circuit. Simulation results demonstrated significant improvements in signal fidelity, power efficiency, and total harmonic distortion (THD) when compared to conventional amplifier designs. Furthermore, the proposed system was implemented in a prototype audio setup, validating its practical applicability and scalability. This research establishes a new paradigm in power amplifier design, highlighting the potential of artificial neural networks to revolutionize audio electronics by providing intelligent control and dynamic optimization. The results underscore the relevance of machine learning techniques in achieving high-performance, energy-efficient, and adaptive audio amplification systems for both consumer and professional applications. The conventional Signal-to-Noise Ratio cause of poor performance and building of power amplifiers for audio systems was 58(dB). Meanwhile, when an ANN technique was integrated into it, it improved it to 69.6 dB. Finally, with the results obtained, the percentage improvement in designing and building of power amplifiers for audio systems when an ANN technique was integrated into it was 20%.},
                  keywords = {Improving efficiency, performance, power amplifiers, audio systems, operation, artificial neural network},
                  month = {March}
        }