2021/06/02 by Syed Saiq Hussain, Muhammad Usman, Hussain, Syed Saiq +9
Computer Science · Engineering · Mathematics · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Neural Networks and Applications #Quantum Algebra (math.QA) #cs.LG #math.QA
paper · pdf · doi:10.48550/arxiv.2106.01370
Article is under review. This is a preprint version
arxiv created 2021/06/02 · openalex publication_date 2021/06/02 · arxiv updated 2021/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed. The proposed method is based on the q-gradient which is also known as Jackson derivative. In contrast to the conventional gradient, which finds the tangent, the q-gradient finds the secant of the function and takes larger steps towards the optimal solution. The proposed q-RBFNN is analyzed for its convergence performance in the context of least square algorithm. In particular, a closed form expression of the Wiener solution is obtained, and stability bounds of the learning rate (step-size) is derived. The analytical results are validated through computer simulation. Additionally, we propose an adaptive technique for the time-varying q-parameter to improve convergence speed with no trade-offs in the steady state performance.