Abstract: A method for changing the CPU frequency under control of a neural network. The neural network has m basis functions and n basis points that are connected together. Using the learning capability of the neural network to deduce basis weights based on dummy environmental parameters and a dummy output vector. In an application procedure, environmental parameters are input to the basis points and basis vectors are calculated based on the basis functions. Integrating the multiplication of each basis vector and its corresponding basis weight, an output vector can be generated to determine a control signal so that the CPU can be controlled to raise or lower its operating frequency. In addition, if the user has to change the parameters due to behavior, a fast learning function of a radial neural network can be used for complying with each user's behavior.

Abstract: A method and system for online plug-and-play (PnP) driver installation is presented. The steps for accomplishing the online PnP driver installation according to the present invention is involved with the following steps of building a driver database in a network host, enabling a data processing machine to establish a connection with the network host through a computer network, executing a driver detection program to generate a detection result, creating a driver package from the driver database in accordance with the detection result being transferred to the network host, and transferring the driver package to the data processing machine and executing the driver package by the data processing machine.

Abstract: A method for changing the CPU frequency under control of a neural network. The neural network has m basis functions and n basis points that are connected together. Using the learning capability of the neural network to deduce basis weights based on dummy environmental parameters and a dummy output vector. In an application procedure, environmental parameters are input to the basis points and basis vectors are calculated based on the basis functions. Integrating the multiplication of each basis vector and its corresponding basis weight, an output vector can be generated to determine a control signal so that the CPU can be controlled to raise or lower its operating frequency. In addition, if the user has to change the parameters due to behavior, a fast learning function of a radial neural network can be used for complying with each user's behavior.