An OFDM Channel Estimation Method with Radial Basis Function Neural Network

For OFDM system, we proposed a channel estimation method based on radial basis function neural network (RBFNN). The neural network with Gaussian basis function is established according to the pilot pattern, where the network parameters are obtained by training channel response of pilot subcarriers as objective values for input samples. With the established network, channel coefficients of non-pilot subcarriers can be predicted. The simulation results indicate that the proposed algorithm performs well in OFDM systems under Rayleigh multipath fading channel.

FUZZY PREDICTIVE CONTROLLER FOR UNKNOWN DISCRETE CHAOTIC SYSTEMS

In this paper, a fuzzy logic-based approach is taken for modeling and prediction-based control of unknown chaotic system using measured input–output data obtained from the underlying system. Under this framework, a Takagi–Sugeno (TS) fuzzy system is used with a general structure of a linear combination of Gaussian basis function in conjunction with the Levenberg–Marquardt algorithm for the optimization of model parameters. A real-time one-pass learning algorithm is developed for identifying the unknown chaotic system. Based on the fuzzy model above, a predictive controller is achieved for the stabilization of the fuzzy model on unknown unstable fixed points. Several simulation examples are included to illustrate the effectiveness and the feasibility of the proposed method for both fuzzy modeling and predictive control phases.

Spatial Representation of Predictive Motor Learning

A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction of motion of an object held in their right hand, which subjects were required to restrain. The results show that predictive learning was local to the direction of forces experienced during learning and that the magnitude of predictive responses was greatly reduced for novel directions of action of the left hand. The pattern of generalization shows that the representation of predictive learning is spatially local and can be approximated as having a spatially narrow Gaussian basis function.