BP Neural Network Implementation for Vehicle Speed Prediction

In this documentation, we present an algorithm implementation called the BP Neural Network code. This algorithm can predict future vehicle speeds by analyzing a series of automotive-related data parameters such as engine output, current velocity, and other relevant metrics. The implementation utilizes a multi-layer feedforward neural network trained with the backpropagation algorithm, which adjusts connection weights through gradient descent minimization to reduce prediction errors. This technology finds applications in various fields including transportation systems and automotive engineering. By employing this BP neural network code, we can gain deeper insights into vehicle operational patterns, thereby enhancing traffic safety and optimizing vehicle performance metrics. Furthermore, the algorithm's learning mechanism helps us better understand automotive working principles, providing valuable data insights and analytical foundations for future vehicle design and research development initiatives. Key functions include data normalization preprocessing, hidden layer activation using sigmoid functions, and iterative weight updates through backward error propagation.