Solution of Definite Integrals using Functional Link Artificial Neural Networks
This provides an alternative computational tool for numerical integration tasks, though it appears incremental as it builds on existing neural network approaches.
The paper tackles the problem of solving definite integrals by proposing a new method using functional link artificial neural networks, achieving effectiveness and precision comparable to existing numerical methods, particularly for higher-order polynomials.
This paper discusses a new method to solve definite integrals using artificial neural networks. The objective is to build a neural network that would be a novel alternative to pre-established numerical methods and with the help of a learning algorithm, be able to solve definite integrals, by minimising a well constructed error function. The proposed algorithm, with respect to existing numerical methods, is effective and precise and well-suited for purposes which require integration of higher order polynomials. The observations have been recorded and illustrated in tabular and graphical form.