Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization

Stochastic optimization naturally arises in machine learning. Efficient algorithms with provable guarantees, however, are still largely missing, when the objective function is nonconvex and the data points are dependent. This paper studies this fundamental challenge through a streaming PCA problem for stationary time series data. Specifically, our goal is to estimate the principle component of time series data with respect to the covariance matrix of the stationary distribution. Computationally, we propose a variant of Oja's algorithm combined with downsampling to control the bias of the stochastic gradient caused by the data dependency. Theoretically, we quantify the uncertainty of our proposed stochastic algorithm based on diffusion approximations. This allows us to prove the asymptotic rate of convergence and further implies near optimal asymptotic sample complexity. Numerical experiments are provided to support our analysis

Design and implementation of an intelligent car obstacle avoidance system based on deep learning

Through the integration of deep learning technology, from the simplest driving method to the realizatio n of the “carnetwork road” interaction, the use of STM32F103 microprocessor control chip, and through the PWM technology to achieve the speed and steering gear regulation, at the same time, the use of deep learning self-cognition technology, so that intelligent vehicles can make selfcognitive decisions like human minds , by looking for the best route to avoid some obstacles on the road surface, and the selection of the optimal forecast route, and through the tracking controller to achieve the black line function, through the anti-collision system to achieve the vehicle detection and obstacle avoidance function