Nonlinear System Modeling with BP Neural Networks - Fitting Nonlinear Functions

Using a simple bivariate quadratic function enables nonlinear fitting with BP neural networks to better approximate actual values. This method is particularly suitable for beginners as it's straightforward and helps understand neural network principles and applications. The implementation typically involves defining network architecture (input/hidden/output layers), using sigmoid activation functions, and applying backpropagation algorithms for weight updates. Through this approach, beginners can learn how to perform nonlinear fitting with quadratic functions while understanding its practical importance. The MATLAB implementation would include defining the quadratic function as target data, initializing network weights randomly, and using training functions like 'trainlm' for Levenberg-Marquardt optimization. I strongly recommend beginners use this simple bivariate quadratic function for BP neural network nonlinear fitting to enhance their comprehension and application capabilities of neural networks.