Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation
This work addresses the need for faster learning algorithms in neural networks, offering a novel control-theoretic approach that is incremental in improving convergence rates.
The paper tackled the problem of optimizing weight updates in feed-forward neural networks by applying the Hamilton-Jacobi-Bellman (HJB) equation to derive closed-form solutions for optimal cost and weight updates, resulting in faster convergence in computational time compared to existing best-performing algorithms on benchmark datasets like 8-bit parity and breast cancer.
A control theoretic approach is presented in this paper for both batch and instantaneous updates of weights in feed-forward neural networks. The popular Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal weight update law. The remarkable contribution in this paper is that closed form solutions for both optimal cost and weight update can be achieved for any feed-forward network using HJB equation in a simple yet elegant manner. The proposed approach has been compared with some of the existing best performing learning algorithms. It is found as expected that the proposed approach is faster in convergence in terms of computational time. Some of the benchmark test data such as 8-bit parity, breast cancer and credit approval, as well as 2D Gabor function have been used to validate our claims. The paper also discusses issues related to global optimization. The limitations of popular deterministic weight update laws are critiqued and the possibility of global optimization using HJB formulation is discussed. It is hoped that the proposed algorithm will bring in a lot of interest in researchers working in developing fast learning algorithms and global optimization.