Seeker-Azimuth Determination with Gyro Rotor and Optoelectronic Sensors.

This paper presents an approach to seeker-azimuth determination using the gyro rotor and optoelectronic sensors. In the proposed method, the gyro rotor is designed with a set of black and white right spherical triangle patterns on its surface. Two pairs of optoelectronic sensors are located symmetrically around the gyro rotor. When there is an azimuth, the stripe width covering the black and white patterns changes. The optoelectronic sensors then capture the reflected optical signals from the different black and white pattern stripes on the gyro rotor and produce the duty ratio signal. The functional relationship between the measured duty ratio and the azimuth information is numerically derived, and, based on this relationship, the azimuth is determined from the measured duty ratio. Experimental results show that the proposed approach produces a large azimuth range and high measurement accuracy with the linearity error of less than 0.005.

Recursive least mean p-power Extreme Learning Machine.

As real industrial processes have measurement samples with noises of different statistical characteristics and obtain the sample one by one usually, on-line sequential learning algorithms which can achieve better learning performance for systems with noises of various statistics are necessary. This paper proposes a new online Extreme Learning Machine (ELM, of Huang et al.) algorithm, namely recursive least mean p-power ELM (RLMP-ELM). In RLMP-ELM, a novel error criterion for cost function, namely the least mean p-power (LMP) error criterion, provides a mechanism to update the output weights sequentially. The LMP error criterion aims to minimize the mean p-power of the error that is the generalization of the mean square error criterion used in the ELM. The proposed on-line learning algorithm is able to provide on-line predictions of variables with noises of different statistics and obtains better performance than ELM and online sequential ELM (OS-ELM) while the non-Gaussian noises impact the processes. Simulations are reported to demonstrate the performance and effectiveness of the proposed methods.

Online sequential fuzzy extreme learning machine for function approximation and classification problems.

In this correspondence, an online sequential fuzzy extreme learning machine (OS-Fuzzy-ELM) has been developed for function approximation and classification problems. The equivalence of a Takagi-Sugeno-Kang (TSK) fuzzy inference system (FIS) to a generalized single hidden-layer feedforward network is shown first, which is then used to develop the OS-Fuzzy-ELM algorithm. This results in a FIS that can handle any bounded nonconstant piecewise continuous membership function. Furthermore, the learning in OS-Fuzzy-ELM can be done with the input data coming in a one-by-one mode or a chunk-by-chunk (a block of data) mode with fixed or varying chunk size. In OS-Fuzzy-ELM, all the antecedent parameters of membership functions are randomly assigned first, and then, the corresponding consequent parameters are determined analytically. Performance comparisons of OS-Fuzzy-ELM with other existing algorithms are presented using real-world benchmark problems in the areas of nonlinear system identification, regression, and classification. The results show that the proposed OS-Fuzzy-ELM produces similar or better accuracies with at least an order-of-magnitude reduction in the training time.